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MICROALGAL OIL SCREENING FOR BIODIESEL

PRODUCTION BY MASS SPECTROMETRIC

TECHNIQUES

Thesis submitted for the partial fulfillment of the degree of

DOCTOR OF PHILOSOPHY

in

CHEMISTRY

by

MUHAMMAD ARIF AHMED

H. E. J. Research Institute of Chemistry

International Center for Chemical and Biological Sciences

University of Karachi

Karachi-75270, Pakistan

2015

Dedicated

To

My Dear Father (late) and

Beloved Wife

THESIS CERTIFICATE

This is to certify that the thesis entitled, “Microalgal oil screening for biodiesel production by mass

spectrometric techniques” has been submitted to the Board of Advance Studies and Research (BASR),

University of Karachi by Mr. Muhammad Arif Ahmed s/o Mr. Muhammad Atiq Ahmed for the

award of the degree of Doctor of Philosophy (Ph.D.) in the discipline of Chemistry. The research has

been carried out under my supervision. I certify that the work submitted is original and not

plagiarized from any other source, except as specified in the references. Neither the thesis nor the work

contained therein has been previously submitted to any institution for a degree.

Dr. Syed Ghulam Musharraf

Associate Professor

H. E. J. Research Institute of Chemistry

International Center for Chemical and Biological Sciences (ICCBS)

University of Karachi

Karachi-75270, Pakistan

iv

Content

Acknowledgements viii

List of Publications ix

List of Tables x

List of Figures xii

List of Abbreviations xvi

Abstract xvii

xviii

CHAPTER # 1: GENERAL INTRODUCTION

1.1 Fuel: World Energy Scenario 2

1.2 Biofuel: An Alternative Fossil Fuel 2

1.3 Biodiesel 3

1.3.1 Biodiesel Sources 4

1.3.2 Biodiesel Production Process 5

1.3.2.1 Extraction of Oil from Biomass 5

1.3.2.2 Conversion of Oil into Biodiesel 6

1.3.2.3 Separation of Biodiesel 7

1.3.2.4 Refining 8

1.3.3 Biodiesel Standards 10

1.4 Microalgae 12

1.5 Oil Productivity of Microalgae 12

1.6 Merits and Demerits of Microalgal Biodiesel 16

1.7 Chemical Analysis of Biodiesel 17

1.8 Mass Spectrometry 20

1.8.1 Ionization Techniques 20

1.8.1.1 Electron Impact (EI) Ionization 20

1.8.1.2 Chemical Ionization (CI) 20

1.8.1.3 Fast Atom Bombardment (FAB) 21

1.8.1.4 Matrix Assisted Laser Desorption/Ionization (MALDI) 21

1.8.1.5 Electrospray Ionization (ESI) 21

1.8.2 Mass Analyzers 22

1.8.2.1 Quadrupole 22

1.8.2.2 Time-of-Flight 23

1.8.2.3 Magnetic Sector 23

1.8.2.4 Quadrupole Ion Trap 23

1.8.2.5 Ion Cyclotron Resonance 24

1.8.2.6 Orbitrap 24

1.8.3 Tandem Mass Spectrometry (MS/MS) 25

1.8.3.1 Types of Tandem Mass Spectrometry 25

1.8.3.2 Methods for the Activation of Ions for MS/MS Analysis 26

1.8.3.3 Scan Modes 28

1.9 Gas Chromatography-Mass spectrometry: A Promising Tool for Oil Analysis 29

CHAPTER 2: OIL CONTENTS SCREENING OF MICROALGAL ISOLATES

FROM SOUTHERN PAKISTAN THROUGH GC-MS FOR BIODIESEL

PRODUCTION

2.1 Background 32

v

2.2 Microalgae Screening Strategy to Explore Potential for Biodiesel Production 33

2.2.1 Sampling 33

2.2.2 Cultivation and Biomass Production 33

2.2.3 Isolation of Pure Cultures 34

2.2.4 Lipid Determination 35

2.2.5 Profiling of Lipid Components 35

2.2.6 Optimization of Lipid and Biomass Productivity for Commercial Scale

Biodiesel Production

35

2.3 Experimental 37

2.3.1 Standards and Chemicals 37

2.3.2 Microalgae Collection, Isolation and Identification 37

2.3.3 Large Scale Cultivation of Miroalgae 39

2.3.4 Lipid Extraction and Derivitization of Oilgae 39

2.3.5 Characterization of Biodiesel 40

2.3.6 GC-MS Analysis 40

2.3.7 GC-MS/MS Quantification of FAMEs 41

2.4 Results and Discussion 42

2.4.1 Microalgae Collection and Identification 42

2.4.2 Cell Biomass Production and Comparison of Lipid Content 44

2.4.3 Characterization of Biodiesel 45

2.4.4 Properties of Biodiesel 50

2.4.5 Quantification of FAMEs in Oilgae by GC-MS/MS 52

2.4.6 Application to Microalgal Biodiesel Samples 54

2.5 Conclusion 55

CHAPTER 3: QUANTIFICATION OF FAMES IN BIODIESEL BLENDS OF

VARIOUS SOURCES BY GAS CHROMATOGRAPHY-TANDEM MASS

SPECTROMETRY

3.1 Biodiesel Blends 58

3.2 Biodiesel Blends Standards 59

3.3 Mass spectrometric quantification using SRM / MRM 60

3.4 FAMEs Analysis of Biodiesel Blends 61

3.5 Experimental 64

3.5.1 Material and Chemicals 64

3.5.2 Preparation of Stock and Calibration Solutions 64

3.5.3 Biodiesel Preparation 65

3.5.4 Blend Preparation 65

3.5.5 Sample Preparation 65

3.5.6 GC-MS/MS Analysis 66

3.5.7 Method Validation 66


3.6.1 Method Optimization 67

3.6.2 Calibration Curves of Standards 76


3.6.3.1 Linearity 83

3.6.3.2 Limit of Detection (LOD) and Limit of Quantification (LOQ) 83

3.6.3.3 Accuracy 84

3.6.3.4 Precision 84

3.6.3.5 Selectivity 84

3.6.4 Application to Biodiesel Samples 87

3.6.4.1 Analysis of Peanut Biodiesel 87

3.6.4.2 Analysis of Rapeseed Biodiesel 87

3.6.4.3 Analysis of Cottonseed Biodiesel 88

3.6.4.4 Analysis of Soybean biodiesel 88

vi

3.6.4.5 Analysis of Sunflower biodiesel 88

3.6.4.6 Analysis of Coconut Biodiesel 89

3.6.4.7 Analysis of Castor Seed Biodiesel 89

3.6.4.8 Analysis of Neem Biodiesel 89

3.6.4.9 Analysis of Linseed Biodiesel 90

3.6.4.10 Analysis of Microlagal Biodiesel 90

3.6.4.11 Analysis of Waste Cooking Oil Biodiesel 90

3.6.4.12 Analysis of Biodiesel-Biodiesel Blends 91

3.7 Conclusion 107

CHAPTER 4: SENSITIVE DETERMINATION OF GLYCEROL BY POST-

DERIVATIZATION USING HPLC-DAD METHOD IN BIODIESEL SAMPLES

4.1 Introduction 109

4.2 Experimental 113

4.2.1 Chemicals and Materials 113

4.2.2 Instrumentations 113

4.2.3 Esterification of Glycerol into Glycerol tribenzoate (GBT) 114

4.2.3.1 Standard Protocol 114

4.2.3.2 Microwave Procedure 115

4.2.4 Purification of GTB 115

4.2.5 Preparation of Stock and Calibration Standard Solutions 115

4.2.6 Preparation of Biodiesel Samples 115

4.2.7 Derivatization of Biodiesel 116



4.2.8.2 Accuracy and Precision 116

4.2.8.3 Robustness 117

4.2.8.4 Recovery Studies 117



4.3.1 Reaction Optimization for Esterification 118

4.3.2 Isolation and Characterization of GTB 120

4.3.3 HPLC Method Optimization. 120

4.3.4 Calibration Curves of Standard GTB 123



4.3.5.2 Precision and Accuracy 125

4.3.5.3 Robustness 127

4.3.5.4 Recovery Studies 128


4.3.6 Analysis of Glycerol in Biodiesel Samples 130

4.3.7 Comparison of Developed HPLC Method with Other Reported Techniques 132

4.5 Conclusion 134

REFERENCES 134

GLOSSARY 149

PERSONAL INTRODUCTION 153

vii

Acknowledgements

My countless thanks to the most Merciful, Omnipotent, and Omniscient Almighty Allah for his

blessings and guidance throughout my life; and never-ending Darood-o-Salam to beloved Holy

Prophet Hazrat Muhammad (ملسو هيلع هللا ىلص) whose teachings enlightened my conscience with the essence of

faith in Allah Tala.

I express my foremost and sincere gratitude to the Founding Director, Prof. Dr. Salimuzzaman

Siddiqui (F.R.S., D.Phil., H.I., S.I.), and wish to acknowledge here, Patron-in-Chief I.C.C.B.S., Prof. Dr.

Atta-ur-Rahman(F.R.S., N.I., H.I., S.I., T.I.), an eminent international scientist, for his tremendous

contribution in developing science and technology in the Pakistan and developing world.

It is indeed a greatest pleasure for me to express my heartiest thanks to Prof. Dr. Muhammad Iqbal

Choudhary(H.I., S.I., T.I.), Director of the H. E. J. Research Institute of Chemistry, International Center

for Chemical and Biological Sciences, University of Karachi, for his support and encouragement.

It is a great privilege for me to express my deep gratitude to my ever-smiling and impressive supervisor

Dr. Syed Ghulam Musharraf, Associate Professor, H. E. J. Research Institute of Chemistry,

International Center for Chemical and Biological Sciences (ICCBS), for his immense knowledge,

guidance, continuous support, enthusiasm, and encouragement throughout my research work. I am

thankful to all faculty members of ICCBS specially Prof. Dr. Viqar Uddin Ahmed (H.I., S.I., Khawarizmi

Laureate), Prof Dr. Khalid M. Khan (T.I., S.I.) and Prof. Dr. Bina S. Siddiqui (T.I., S.I., Khawarizmi Laureate).

I am pleased with my lab. fellows for the most memorable moments together; and their cooperation,

support, and kindness during my research work. I am thankful to Dr. Naghma Hashmi, Dr. Urooj

Fatima, Dr. Madiha Goher, Dr. Naveed Iqbal, Mr. Arslan Ali, Mr. Irfan Akram, Mr. Jalal ud din,

Ms. Nayab Kanwal, Ms. Aisha Bibi, Mr. Qamar-ul-Arfeen, Dr. Shumaila Mazher, Ms Amna Jabbar

Siddiqui, Ms. Mahwish Saleem, Ms. Najia Shahid, Mr. Umair Gulzar, Ms. Iffat Azeem, Mr.

Muhammad Salman Bhatti, Mr. Kashif and Ms. Ayesha Khalid. My special appreciation is to my lab

attendant Mr. Shehzad for his cooperation, and dedication to work.

I am very grateful to all the technicians of mass spectrometry laboratory, particularly Ms. Afshan,

Mr. Danish, and Ms. Sidra. I am also thankful to all technical and non-technical staff members of

the institute. I want to acknowledge here the financial support from Higher Education Commission

(H.E.C.) for my research work (Project # 201-1013-R & D). I want to acknowledge research

officerMs. Noureen Zehra (Taxonomist) on the HEC project for biological work in my thesis.

Finally, special recognition goes out to my family, where words fail to express my thankfulness to my

beloved parent, siblings and children. I am honour-bound to my father Mr. Muhammad Atiq Ahmed

(Late) for his support and my mother‟s ever-lasting prayers. I would like to extend my sincerest thank

to my beloved wife without her patience, love, and sacrifice, this work could have not been possible.

At last I pray to ALLAH TALA for brightening future and good health to all my teachers, family

members, and friends.

Aamin! Muhammad Arif Ahmed

2015, Karachi.

viii

List of Publications

1. Musharraf, S. G., Ahmed, M. A. and Zehra, N. (2015). Quantification of FAMEs in

biodiesel blends of various sources by gas chromatography tandem mass spectrometry.

Analytical Methods, 7(8), 3372-3378.

2. Musharraf, S. G., Ahmed, M. A., Zehra, N., Kabir, N., Choudhary, M. I. and Rahman, A.

U. (2012). Biodiesel production from microalgal isolates of southern Pakistan and

quantification of FAMEs by GC-MS/MS analysis. Chemistry Central Journal, 6, 149.

3. Musharraf, S. G., Iqbal, N., Ahmed, M. A., Mazhar, S. and Choudhary, M. I. (2011).

Screening of E- and Z-guggulsterones in the gum-resin exudates of some common plants

and method validation in raw, extracted, and pharmaceutical formulations of commiphora

mukul by HPLC. Journal of Liquid Chromatography and Related Technologies, 34(18),

2103-2117.

4. Musharraf, S. G., Ahmed, M. A., Ali, R. A. and Choudhary, M. I. (2011). Hydroxylation

of (+)-menthol by Macrophomina phaseolina. Biocatalysis and Biotransformation, 29(2-

3), 77-82.

5. Ahmed, M. A., Khan I., Hashim J. and Musharraf, S. G. Sensitive determination of

glycerol by post-derivatization using HPLC-DAD method in biodiesel samples.

(submitted inAnalytical Methods).

ix

List of Tables

Table 1: ASTM biodiesel fuel standards. 10

Table 2: European committee for standardization EN 14214 biodiesel fuel standards. 11

Table 3: Comparison of microalgae with other biodiesel feed stocks. 13

Table 4: Typical ranges of lipid content and productivity of selected marine and

freshwater microalgal species.

14

Table 5: Summary of the different properties of frequently used mass spectrometric

ionization techniques.

22

Table 6: Comparison of various mass analyzers. 25

Table 7: Media composition of Bold‟s basal medium. 38

Table 8: Media composition of Guillard f/2 medium. 39

Table 9: Optimized GC-MS/MS acquisition parameters for FAMEs. 41

Table 10: Summary of samples collection. 42

Table 11: Biomass and algal oil productivity of various microalgal species. 45

Table 12: FAMEs profile of biodiesel of various microalgal species by GC-MS. 48

Table 13: Properties of biodiesel obtained from various microalgae. 51

Table 14: Retention time, correlation coefficients, LOD and LOQ of FAMEs. 53

Table 15: Absolute amount of FAMEs analysis of oilgae. 55

Table 16: ASTM specification for biodiesel-diesel blends. 59

Table 17: Literature survey of FAMEs analysis in biodiesel blends. 62

Table 18: Optimized GC–MS/MS acquisition method parameters and list of precursor

ions and product ions of each FAMEs.

70

Table 19: Concentration ranges of each FAME for calibration standards. 76

Table 20: Retention time, correlation coefficient, regression equation, LOD and LOQ

of individual FAMEs.

85

Table 21: Precision and accuracy for all FAMEs standards in QC samples. 86

Table 22: Amount of FAMEs in various biodiesels and their blends. 104

Table 23: Alternative methods for determination of free glycerol in biodiesel. 110

Table 24: Esterification of glycerol into glyceryl tribenzoate . 119

x

Table 25: Optimized gradient mobile phase. 122

Table 26: Calibration data of GTB. . 123

Table 27: Linear regression data for the calibration curves. 124

Table 28: Intra-day and inter-day analysis of GTB. 126

Table 29: Summary of the robustness parameters of developed method. 127

Table 30: Recovery studies of GTB. 128

Table 31: Analysis of biodiesel samples. 130

Table 32: Comparison of glycerol analysis using HPLC-DAD after post derivatization

with other techniques.

132

xi

List of Figures

Figure 1: Schematic design of (A) Transesterification and (B) Soap formation. 8

Figure 2: Schematic diagram of biodiesel production process. 9

Figure 3: Microalgae screening strategy to explore potential for biodiesel production. 36

Figure 4: Differential interference contrast (DIC) images of isolated unimicroalgal

species (A) Scenedesmus quadricauda (B) Scenedesmus acuminatus (C)

Nannochloropsis sp. (D) Anabaena sp. (E) Chlorella sp. and (F)

Oscillatoria sp.

43

Figure 5: Total ion chromatogram (TIC) of biodiesel synthesized from microalgal oil

(A) Scenedesmus quadricauda, (B) Scenedesmus acuminatus, (C)

Nannochloropsis sp., (D) Anabaena sp., (E) Chlorella sp. and (F)

Oscillatoria sp.

46

Figure 6: Distribution of FAMEs among various microagal strains. 50

Figure 7: (A) Full scan MS chromatogram of biodiesel synthesized from microalgal

oil Reconstructed ion chromatogram for (B) C-14:0 at m/z

242.4→72.7+100+157.1 (C) C-16:0 at m/z 270.4→100+58.6+132 (D) C-

18:0 at m/z 298.5 →72+101+100.8+198.

52

Figure 8: Calibration curve of FAMEs (C-14:0, C-16:0 and C-18:0). 54

Figure 9: MRM chromatogram of FAMEs standards. 68

Figure 10: Extracted ion chromatograms and product ions spectra (quantifier and

qualifiers) of FAMEs (C-6:0, C-8:0, C-10:0, C-12:0 and C-14:0).

71


qualifiers) of FAMEs (C-14:1 ∆cis-9

, C-16:0, C-16:1 ∆cis-9

, C-18:0 and C-

18:1 ∆cis-9

).

72



, C-18:2 ∆cis, cis-9,12

, C-18:3 ∆cis-9,12,15

, C-

20:0 and C-20:1 ∆cis-11

).

73


qualifiers) of FAMEs (C-22:0, C-22:1 ∆cis-13

, C-24:0 and C-24:1 ∆cis-15

).

74

Figure 14: (A) and (B): TIC scan and MRM of diesel, respectively; (C) and (D): TIC

scan and MRM of B-2 biodiesel-diesel blend, respectively; (E) and (F):

TIC scan and MRM of B-5 biodiesel-diesel blend, respectively; (G) and

(H): TIC scan and MRM of B-20 biodiesel-diesel blend, respectively; (I)

and (J): EI TIC scan and MRM of Peanut biodiesel (B-100), respectively.

75

Figure 15: Calibration curve of FAMEs (C-6:0, C-8:0, C-10:0, C-12:0 and C-14:0). 77

Figure 16: Calibration curve of FAMEs (C-12:0, C-14:0 and C-14:1 ∆cis-9

). 78

xii

Figure 17: Calibration curve of FAMEs (C-16:0, C-16:1 ∆cis-9

and C-18:0). 79

Figure 18: Calibration curve of FAMEs (C-18:1 ∆cis-9

, C-18:1 ∆cis-11

, C-18:2 ∆cis, cis-9,12

).

80

Figure 19: Calibration curve of FAMEs (C-18:3 ∆cis-9,12,15

, C-20:0 and C-20:1 ∆cis-11

). 81


and C-24:0). 82

Figure 21: Calibration curve of FAME C-24:1 ∆cis-15

. 83

Figure 22: MRM chromatogram of Peanut (A) B-2, (B) B-5, (C) B-20 and (D) B-100. 92

Figure 23: MRM chromatogram of Rapeseed (A) B-2, (B) B-5, (C) B-20 and (D) B-

100.

93

Figure 24: MRM chromatogram of Cotton seed (A) B-2, (B) B-5, (C) B-20 and (D) B-

100.

94

Figure 25: MRM chromatogram of Soybean (A) B-2, (B) B-5, (C) B-20 and (D) B-

100.

95

Figure 26: MRM chromatogram of Sunflower (A) B-2, (B) B-5, (C) B-20 and (D) B-

100.

96

Figure 27: MRM chromatogram of Coconut (A) B-2, (B) B-5, (C) B-20 and (D) B-

100.

97

Figure 28: MRM chromatogram of Castor seed (A) B-2, (B) B-5, (C) B-20 and (D) B-

100.

98

Figure 29: MRM chromatogram of Neem seed (A) B-2, (B) B-5, (C) B-20 and (D) B-

100.

99

Figure 30: MRM chromatogram of Linseed (A) B-2, (B) B-5, (C) B-20 and (D) B-

100.

100

Figure 31: MRM chromatogram of Microalgae (A) B-2, (B) B-5, (C) B-20 and (D) B-

100.

101

Figure 32: MRM chromatogram of waste cooking oil (A) B-2, (B) B-5, (C) B-20 and

(D) B-100.

103

Figure 33: MRM chromatogram of biodiesel-biodiesel-diesel blends (A) BB-2, (B)

BB-5, (C) BB-20 and biodiesel-diesel blends (D) BB-100.

103

Figure 34: HPLC-ELSD chromatogram of (A) standard glycerol and (B) reaction

mixture.

120

Figure 35: UV scan for GTB. 122

Figure 36: Chromatogram of (A) standard (B) blank (C) derivatized standard glycerol

and (D) derivitized sunflower biodiesel.

122

Figure 37: Calibration curve of GTB. 124

xiii

Figure 38: Overlaid chromatograms of standard (blue), microagal biodiesel (green),

peanut biodiesels (red) and sunflower biodiesel (pink).

129

Figure 39: Chromatogram of microalgal (A), sunflower (B) and peanut (C) biodiesel

samples.

131

xiv

List of Abbreviations

ASTM American Society for Testing and Materials

B-2 2% biodiesel 98%diesel blend

B-5 5% biodiesel 95% diesel blend

B-20 20% biodiesel 80% diesel blend

B-100/ B100 Pure biodiesel

CI Chemical ionization

CID Collision-induced dissociation

CE Capillary electrophoresis

DAD Diodearraydetector

DAG Diacyl glycerol

ECD Electron capture dissociation

EI Electron Ionization

ELSD Evaporative light scattering detections

ESI-MS Electrospray ionization mass spectrometry

EtOAc Ethyl acetetate

EU European Union

FAB-MS Fast atom bombardment-mass spectrometry

FAAE Fatty acid alkyl ester

FAME Fatty acid methyl ester

FAEE Fatty acid ethyl ester

FFA Free fatty acid

FAMEs Fatty acid methyl esters

GC Gas chromatography

GC-FID Gas chromatography-flame ionization detector

GC-MS Gas chromatography-mass spectrometry

GC-MS/MS Gas chromatography-tandem mass spectrometry

GDB Glyceryl dibenzoate

GMB Glyceryl monobenzoate

GTB Glyceryl tribenzoate

HPLC High performance liquid chromatography

HHV Higher heating value

xv

LOD Limit of detection

LOQ Limit of quantification

MALDI Matrix-assisted laser desorption ionization

MAG Monoacyl glycerol

MS-MS Tandem mass spectrometry

MRM Multiple reactions monitoring

m/z Mass-to-charge ratio

PAH Poly aromatic hydrocarbon

PCSIR Pakistan council of scientific and industrial research

PDA Photodiodearraydetector

QIT Quadrupole ion trap

QqQ Triple quadrupole

QqTOF Quadropole time-of-flight

SFC Supercritical fluid chromatography

SIM Selected ion monitoring

SPE Solid phase extraction

SRM Selected reaction monitoring

TAG Triacyl glycerol

TIC Total ion chromatogram

TLC Thin layer chromatography

RI Refractive index

xvi

Abstract

The use of alternative fuel for diesel engines is accelerated by the energy crisis due to

depletion of resources and increased environmental problems. Microalgae have attracted

major interest as a sustainable source for biodiesel production on commercial scale. This

dissertation is comprised of four chapters. The first chapter describes an updated literature

survey on biodiesel and advancement and applications of mass spectrometry particularly in

biodiesel analysis.

The second chapter describes the screening of six microalgal species, Scenedesmus

quadricauda, Scenedesmus acuminatus, Nannochloropsis sp., Anabaena sp., Chlorella sp.

and Oscillatoria sp., isolated from fresh and marine water resources of southern Pakistan for

biodiesel production and the GC-MS analysis of their fatty acid methyl esters (FAMEs).

Fatty acid profiling of the biodiesel, obtained from various microalgal oils showed high

content of C-16:0, C-18:0, C-18:1∆cis-9, C-18:1 ∆cis-11 and 10-hydroxyoctadecanoic acid. A

method for absolute quantification of three important saturated FAMEs (C-14:0, C-16:0 and

C-18:0) by GC-MS/MS using multiple reactions monitoring (MRM) mode was employed in

the biodiesel samples obtained from various microalgal oils. The results suggested that

locally found microalgae can be sustainably harvested for the production of biodiesel at

commercial scale. This offers the tremendous economic opportunity for an energy-deficient

nation like Pakistan [Chemistry Central Journal 2012, 6:149].

The third chapter describes a quantification method for saturated and unsaturated FAMEs by

GC-MS/MS in MRM.The developed method was validated in the various biodiesels and

their B-2, B-5 and B-20 blends. The procedure is sensitive, selective and reproducible. The

main advantage of the developed GC-MS/MS method is the effective separation and

quantification of FAMEs in very complex samples of biodiesel-diesel blends and biodiesel-

biodiesel blends [Analytical Methods 2015, 7(8):3372].

The forth chapter describes the quantification of glycerol in various biodiesel samples

through post-derivatization by HPLC using diode array detector. Glycerol was converted

into a UV active product i.e. Glyceryl tribenzoate through simple and effective esterification

using benzoyl chloride and copper chloride as catalyst under mild condition. The developed

HPLC-DAD method is sensitive, selective, reproducible and successfully applied for the

quantification of glycerol in pure biodiesel [Submitted in Analytical Methods].

xvii

ڈیسل اًجٌوں کے لئے هتجبدل ایٌذھي کے ثڑ ھتے ہوئےاضتعوبل کی وجہ ، وضبئل کی کوی

کی پیذاوار ثب ئیو ڈیسلکو تجبرتی ثٌیبدوں پرکب ئی. اور ثڑھتے ہوئے هبحولیبتی هطبئل ہیں

کے لئے ایک پبئیذار رریعہ کے طور پر ثڑی توجہ حبطل ہوئ ہے۔ یہ همبلہ چبر اثواة

، طیف ادة ضروے، اش کی ترلیتبزٍ تریي کبثب ئیو ڈیسلپر هشتول ہے پہلے ثبة هیں

دوضرے .تجسیہ کے لئےهیں، ثیبى کرتب ہےثب ئیو ڈیسل ایپلی کیشٌس خبص طور کی هب ئیپ

,Scenedesmusquadricauda, Scenedesmus acuminatus پرجبتیوںکب ئی کی ثبة هیں چھ

Nannochloropsis sp., Anabaena sp., Chlorella sp. اور Oscillatoria sp. کباى ا ور جب ًچ کیGC-

MSضے ٖٖ ٖ FAMEs ٍتجسیہ کو ثیبى کیب گیب ہے۔ اى پرجبتیوں کوجٌوثی پبکطتبى کے تبز

ا ًواع الطب م کی کب ئی پیذاوارکےلئےالگ کیب گیب۔ کیثب ئیو ڈیسلاور ضوٌذری پبًی ضے

تجسیہ ظبہر کرتب ہےکہ اش شحوی ترشہ کب ،کےثب ئیو ڈیسل ضے حبطل کیب کے روغي

∆C-16:0, C-18:0, C-18:1هیں ثڑی همذار هیںcis-9

, C-18:1 ∆cis-11 10اور-

hydroxyoctadecanoicacidشذٍتیي اہن ضیر.هوجود ہیں ٖٖFAMEsجي هیںC-16:0 ,C-14:0 اور

C-18:0همذاری ًووًوں هیں هطلك ثب ئیو ڈیسل تیلکےکب ئی کےی هختلف شبهل ہیں، ک

کیب وضعطریمہ کبر GC-MS/MSهوڈ اضتعوبل کرتے ہوئےایکMRMکے لئےتجسیہ

تجبرتی اورپبئیذار کب ئی کو گیب۔ًتبئج ظبہر کر تےہیں کہ همبهی طور پر پبئےجبًےوالے

کی پیذاوار کے لئے کبشت کیب جب ضکتب ہے۔ پیش کردٍ تجویستواًبئی ثب ئیو ڈیسل پیوبًے پر

]ثحراى کب شکبر لوم جیطےپبکطتبى کیلئے زثردضت التظبدی هوالع فراہن کرتب ہے

.[۲۰۱۲ ،۱۴۶:۶کیوطٹری وضطی جرًل

ثب ئیو ڈیسلیکجب B-20اور B-5, B-2 ًووًوں جص ثب ئیو ڈیسل ثبة هختلفیکجب اتیطر

هوڈ MRM کے لئےهمذاریيتعFAMEs شذٍ ضیرغیر اور شذٍ ضیرشبهل ہیں ،هیںًووًے

طریمہ کبر کو وضع کرًب اور اش کی توثیك کو GC-MS/MSاضتعوبل کرتے ہوئے ایک

-GCوضع یبفتہ. ہے ر تجذیذ پسیر طریمہ کبر ، حطبش هٌتخت او. ثیبى کرتب ہے

MS/MSثب -ثب ئیو ڈیسل وریکجب ڈیسال-ثب ئیو ڈیسل فبئذٍ یہ ہے کہ یہ یکجباہنطریمہ کبر کب

همذاری کی هوثر علیحذگی اور اى کی FAMEs ثہت پیچیذٍ ًووًوں هیںے کئیو ڈیسل

.[۲۰۱۵، (۸)۷: ۳۳۷۲تجسیبتی طریمے ]رضکتب ہے کتجسیہًووًوں هیں ثب ئیو ڈیسل آلہ کو اضتعوبل کرتے ہوئے هختلفHPLC–DADچو تھب ثبة

. کو ثیبى کرتب ہےهمذاری تجسیہی رریعے گالئیطرول ک هب ثعذ اشتمبق کےضے

ثطورCopper chloride اورBenzoyl chlorideط کے تحتائشر هعتذل گالئیطرولکو

رریعے ایک اضترائذی تبلیف کے اضتعوبل کرتے ہوئے ضبدٍ اور هؤثر عول اًگیس

-HPLCوضع یبفتہ . هیں ثذل دیب گیبGlyceryl tribenzoateیعٌی ثبال ئے ثٌفشی حب طلہ

DAD،اور کبهیبثی کے ضبتھ ہےتجذیذ پسیریرکھتب طریمہ کبر، حطبش هٌتخت

کے لئے لب ثل اضتعوبل همذاری تجسیہے هیں گالئیطرول کثب ئیو ڈیسلخبلض

.(تجسیبتی طریموں هیں پیش)ہے

1

CHAPTER 1

GENERAL INTRODUCTION

Chapter 1: General Introduction

Page | 2

1.1 Fuel: World Energy Scenario

It is estimated that known petroleum reserves would be depleted in less than 50 years at

the present rate of consumption (Issariyakul and Dalai, 2014). Therefore, many recent

research programs focus on the development of concepts such as renewable resources,

sustainable development, green energy, eco-friendly process, etc. Currently, 86% of the

energy being consumed worldwide and nearly 100% of energy desired in the

transportation sector is provided by non-renewable fossil fuels (Vijayaraj and

Sathiyagnanam, 2013). In developed countries, increased demand for energy, price hike

of crude oil, global warming due to emission of greenhouse gases, environmental

pollution, and fast diminishing supply of fossil fuels are the major key factors leading to

search for alternative sources of energy. Some of the most notable alternative sources of

energy capable of replacing fuels include water, solar and wind energy, and biofuels

(Skevis, 2010).

1.2 Biofuel: An Alternative Fossil Fuel

A biofuel is a fuel that contains energy from geologically carbon fixation. These fuels are

produced from living organisms. The term biofuel is used to refer to liquid or gaseous

fuels that are predominantly produced from biomass. Biomass appears to be an attractive

feedstock for three main reasons.

It is a renewable resource that could be sustainably developed in the future.

It has effectively positive environmental properties.

It appears to have significant economic potential (Cadenas and Cabezudo, 1998)

Biomass can be converted into liquid and gaseous fuels through thermochemical and

biological routes. Presently, there are three types of biofuel: bioethanol (absolute ethyl

alcohol obtained by fermentation of sugars by microorganisms) and biobutanol

(fermentation using solvatogenicClostridia sp.); biogas including synthesisgas (mixture

of CO and H2), 2,5dimethylfuran and biodiesel.

United States and European Union (EU) supported the biofuel production with the

objective of increasing fuel supply sources, boosting decarbonisation of fuels for

transportation, decreasing hazardous gaseous emission which causes global warming


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effects, providing more earning opportunities in rural communities and developing long

term plan for finite fossil fuels replacement. Presently several countries such as United

States, Germany, Australia, Italy, Austria, Brazil, Malaysia, Belgium, Netherlands,

Philippines, Spain, Argentina and Indonesia are already using biofuels such as biodiesel

and bioethanol. This trend is expected to continue worldwide with more countries to use

biofuels as source of energy (Balat and Balat, 2010) because the demand for petroleum

is continuously increasing and it is predicted to increase 40% by 2025 (K. Sumithrabai,

2012). Pakistan is already suffering from energy crisis,despite that there is no significant

initiative for biofuel production at commercial scale.

1.3 Biodiesel

Biodiesel is defined as technical regulation set by European Union as EN14214 or by

USA ASTM 6751.

“Biodiesel is the commercial name of diesel engine fuel produced by transesterification

of vegetable oil or fats with alcohol in the presence of catalyst which can be acid or

base.”

The name “biodiesel” has been given to transesterified vegetable oil to describe its use as

a diesel fuel. The so-called biodiesel fuels are oil esters of a biological origin (Khan and

el Dessouky, 2009).

Biodiesel has many advantagesover petroleum based fuel. These advantages are

following:

Biodiesel is non-toxic and degrades four times faster than diesel.

Its oxygen content improves the bio-degradation process. Pure biodiesel degrades

85–88% in water only within 28 days.

Blending of biodiesel with diesel fuel increases engine efficiency.

Biodiesel is safer to handle and store than petroleum diesel because it has a lower

vapour pressure and higher flash point.

Oxygen content of biodiesel improves the combustion process and decreases its

oxidation potential.

The uses of biodiesel can extend the life of diesel engine because it has more


Page | 4

lubricating property than petroleum diesel fuel.

Provides a domestic, renewable, and potentially inexhaustible source of energy

with energy content close to diesel fuel.

Biodiesel obtained from crops produces favourable effects on the environment,

such as decrease in acid rain and greenhouse effect caused by pollution.

Biodiesel is termed as a „„carbon neutral‟‟ as biodiesel yielding plants absorbs

more carbon dioxide from the atmosphere during the process of photosynthesis

than they release CO2 to the atmosphere when used as fuel in engines.

Biodiesel can be used alone or mixed in any ratio with petroleum diesel fuel.

Biodiesel is better than diesel in terms of sulphur content.

It helps to reduce a country‟s reliance on crude oil imports and support

agriculture by providing employment and market opportunities for domestic

crops.

The risk of handling, transporting and storing of biodiesel are lower than petro-

diesel.

The larger reductions in poly aromatic hydrocarbon (PAH) as biodiesel has no

aromatics and no PAH compounds.

By-product i.e. crude glycerol obtained from transesterification process can be

used for manufacturing medical and industrial chemicals(Bozbas, 2008; Fischer,

2002; Fu, 2003; Hanna and Isom, 2009; Inamdar, 2006; Kijenski and

Walisiewicz-Niedbalska, 2006; Subramaniam, Murugesan, Avinash, and

Kumaravel, 2013).

1.3.1 Biodiesel Sources

Biodiesel can be processed from many types of oil obtained from most of the biological

sources. Some of them mentioned below:

1. Food Vegetable Oils: such as soybean, canola, palm, sunflower, peanut etc.

2. Nonfood Vegetable Oils: Jatropha, neem seeds, cotton seed, halophytes etc.

3. Animal Fat: such as lard, tallow, chicken fat, fish oils and insects etc.


Page | 5

4. Fungal Oil: such as Lipomyces starkeyi, Yarrowia lipolytica, Candida albican,

Saccharomyces cervisiae, Torulopsis candida, Lipomyces starkeyi etc.

5. Algal Oils: Macroalgae such as Cladophora and microalgae such as Chlorella

sp. Nannochloris sp. Botryococcus braunii etc.

6. Used Cooking Oils from restaurants(Aveni and Rana, 2008; Canakci and Sanli,

2008; Khandelwal and Rita, 2012; Mandolesi de Araujo, de Andrade, de Souza e.

Silva, and Dupas, 2013)

Biodiesel production from a variety of vegetable oil is called first generation technology.

From 2006 the world prices for corn, wheat, soybeans and some other vegetable oil have

risen due to their use for biodiesel production. Second-generation technology includes

Jatropha. It was cultivated for biodiesel production in many part of the world but recently

reported that Jatropha plant extracts possess several toxic activities including cytotoxic,

insecticidal, moluscicidal, rodenticidal and antimicrobial piscicidal effects (R. K.

Devappa, H. P. S. Makkar and K. Becker, 2010). It also exerts adverse effects on many

animals including poultry, ruminants and rats. There are several cases documented of

seeds toxicity of jatropha due to their accidentally consumption by young children (R. K.

Devappa, H. P. Makkar and K. Becker, 2010). Biodiesel from the from first and second

generation sources are being produced inincreasing amounts, but large scale production

is not sustainable therefore an alternative is offered as thirdgeneration technologyto

minimize the potentially harmful environmental and agricultural consequences

associatedwith current land-based-biofuel feed stocks which include microalgae (Jones

and Mayfieldt, 2012).

1.3.2 Biodiesel Production Process

Biodiesel production starts from biomass production of a biodiesel source. The

schematic diagram of biodiesel production process is shown in figure 1. The detail of

each step is discussed below:

1.3.2.1 Extraction of Oil from Biomass

There are several methods for the extraction of oil from biomass. Some of the recently

used methods are discussed below:


Page | 6

Expeller Pressed: In this process oils are mechanically pressed from the plant material

at high pressure to obtain maximum yield. Not all expeller pressed oils are cold pressed

as high pressure extraction can cause temperatures to rise above 120 degrees. Only if

temperature is monitored and kept under 120 degrees, can the oil be called cold pressed.

Solvent Extraction: In this method, solvent is used to extract the oil from certain seeds,

nuts or kernels in order to make the extraction cost effective. Once the oil has been

obtained, the solvent is then removed from the oil, but a trace percentage of the solvent

may still be present in the final oil. Coconut, palm, grape seed and rice bran are typically

solvent extracted.

CO2 Extracted:In this process, oils are extracted using fluid carbon dioxide as the

solvent. Carbon dioxide is converted into liquid using critical temperature and pressure.

Liquid CO2 is a safe and effective solvent that allows to extract the desirable active

constituents of a plant without the risk of degradation. Once the extraction is completed,

the pressure is released allowing the carbon dioxide to return to its natural gaseous state,

leaving behind only the extracted part of the plant. CO2 extracted oils are the closest

representation of the natural plant ever achieved.

1.3.2.2 Conversion of Oil into Biodiesel

The problems with substituting triglycerides for diesel fuels are mostly associated with

their high viscosities, low volatilities, and polyunsaturated character (Srivastava and

Prasad, 2000). Therefore, there are four different ways to overcome these problems:

Dilution with hydrocarbons (blending)

Emulsification

Pyrolysis (thermal cracking)

Transesterification (alcoholysis)

The most common way of producing biodiesel is the transesterification of vegetable oils

and animal fats.

Transesterification: Vegetable oils are chemicallytriglycerides: which are the triester of

fatty acids and glycerine. Fatty acids have the carboxylic acid (COOH) group and a long

chain (C-6 to C-24) of hydrocarbons. The numbers of carbons are usually event. Some


Page | 7

fatty acid has one or more double bonds usually cis in orientation. The chemical reaction

in which big and branched triglycerides are converted into minor and straight chain

molecules by using alcohol in the presences of catalyst is known as “Transesterification”.

Diglyceride and monoacylgride are also converted into fatty acid alkyl ester (FAAE) by

transetrification process (Figure 2A). The products molecules have similar size to the

molecules of diesel fuel (Ahmad et al., 2010; Defilippis, Giavarini, Scarsella, and

Sorrentino, 1995).

Calculation shows that each mole of triglycerides reacts with three moles of alcohol but it

is also reported that to obtain maximum biodiesel yield, molar ratio of alcohol should be

much higher. Methanol is commonly used for biodiesel production whereas ethanol and

propanol were also used to limited extent. There are two types of catalysts are being used

for biodiesel productionone is homogenous and the other heterogeneous. thesecatalysts

includes acids and bases, sugars, lipases, ion exchange resins, zeolites, and other

heterogeneous materials. Biodiesel is produced at commercial scale by using

homogenous basic catalysts such as NaOH,KOH and NaOMe. The transesterification

reaction is usually faster, cheap, and givemaximum yield with these homogenous basic

catalysts as compared to acid catalysts. The biodiesel production at commercial scale

uses sodium methoxide as catalyst, becausehydroxides can form water by reacting with

alcohol whereas methoxide cannot react with alcholol(Zhou and Boocock, 2006). Rate of

transesterification reaction using homogenous base catalystis about 4,000 times faster as

compared to using acid catalyst for transesterification. Moreover, transeterificaton using

base catalyzed reactions are donot requirehigher temperatures, pressures, and reaction

times. Furthermore, they are not much corrosive to industrial equipment as acid

catalysts(Ayhan Demirbas, 2008). If free fatty acids are present in the oil, they are

transformedinto soap by reacting with base such as KOH or NaOH and the reaction is

called saponifcation reaction (Figure 2B).

1.3.2.1 Separation of Biodiesel

After transesterification reaction, biodiesel is first separated from glycerol before being

subjected to other refining processes. The separation of biodiesel from by-product,

glycerol is usually fast due to difference in their densities. This separation process is

usually performed by either decantation or centrifugation. (Hoffmann, Charette and

Stroobant, 1996; Saleh, Tremblay and Dube, 2010). In decantation separation technique,


Page | 8

the mixture of biodiesel and glycerol is rested in the tank. Although the cost of

separation is low, however the process is slow and inefficient. On the other hand, in

centrifugation the mixture of biodiesel and glycerol is sepersted by centrifuge machine.

The process of separation is fast, but its cost of operation is considerably high (Atadashi,

Aroua, and Aziz, 2010)

Figure 2:(A) Transesterification (B) Soap formation.

1.3.2.2 Refining

Crude biodiesel contained contaminants such as free residual catalyst, soap, and excess

alcohol, therefore purification of crude biodiesel is required to make the fuel for diesel

engine consumption and conformable to the international standard specifications (ASTM

6751 and EN 14214) as shown in Table 1. Refining techniques include such as washing

with hot distilled water, petroleum ether and then washing with distilled water, and

neutralization with H2SO4 (1:1). Among these techniques, washing with hot distilled

water at 50 oC has proved as the best refining option and provides almost 99% pure

biodiesel while moisture from the purified biodiesel was removed using silica gel

crystals.


Page | 9

Figure 1: Schematic diagram of biodiesel production process.


Page | 10

1.3.3 Biodiesel Standards

Biodiesel standards have been implemented in a number of countries in an effort to

ensure that only high quality biodiesel reaches the market place. The two most important

biodiesel standard organizations areAmerican Society for Testing and Materials (ASTM

D6751) in the United States and European Committee for Standardization(EN 14214)in

the European Union. Their speciations for biodiesel are summarized in Tables 1 and 2,

respectively.

Table 1: ASTM D6751 biodiesel fuel standards.

Property Test Method Limits Units

Calcium and magnesium combined EN14538 5 max ppm

Flash point D93 93.0 min °C

Water and sediment D2709 0.050 max vol %

Kinematic viscosity, 40°C D445 1.9-6.0 mm2/s

Sulfated ash D874 0.020 max % mass

Sulfur D5453 0.0015 max (S15)

0.05 max (S500)

% mass

Copper strip corrosion D130 0.020 max -

Cetane number D613 47 min -

Cloud point D2500 Report to customer °C

Carbon residue D4530 0.050 max % mass

Acid number D664 0.50 max mg KOH/g

Free glycerin D6584 0.020 % mass

Total glycerin D6584 0.240 % mass

Phosphorus content D4951 0.001 max % mass

Distillation temperature,

90% recovered (T90) D1160 360 max °C

Oxidation stability EN15751 3 min hours

Cold soak filterability D7501 360 max seconds


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Table 2: European committee for standardization EN 14214 biodiesel fuel standards.

Property Test method EN 14214 Unit

Ester content EN 14103 96.5 min % (mol/mol)

Density at 15°C EN ISO 3675,

EN ISO 12185 860-900

kg/m3

Viscosity at 40°C EN ISO 3104,

ISO 3105 3.5-5.0

mm2/s

Flash point EN ISO 3679 120 min °C

Sulfur content EN ISO 20846;

EN ISO 20884

10.0 max mg/kg

Carbon residue

(10% distillation residue)

EN ISO 10370 0.30 max % (mol/mol)

Cetane number EN ISO 5165 51 min

Sulfated ash ISO 3987 0.02 max % (mol/mol)

Water content EN ISO 12937 500 max mg/kg

Total contamination EN 12662 24 max mg/kg

Copper strip corrosion

(3h, 50°C)

EN ISO 2160 1 degree of corrosion

Oxidative stability, 110°C EN 14112 6.0 min h

Acid value EN 14104 0.50 max mg KOH/g

Iodine value EN 14111 120 max g I2/100 g

Linolenic acid content EN 14103 12.0 max % (mol/mol)

Content of FAME with ≥4

double bonds

1 max % (mol/mol)

Methanol content EN 14110 0.20 max % (mol/mol)

MAG content EN 14105 0.80 max % (mol/mol)

DAG content EN 14105 0.20 max % (mol/mol)

TAG content EN 14105 0.20 max % (mol/mol)

Free glycerine EN 14105,

EN 14106

0.020 max % (mol/mol)

Total glycerine EN 14105 0.25 max % (mol/mol)

Group I metals (Na + K) EN 14108,

EN 14109

5.0 max mg/kg

Group II (Ca + Mg) prEN 14538 5.0 max mg/kg

Phosphorus content EN 14107 10.0 max mg/kg

Cold filter plugging point EN 116 -

°C

Pour point ISO 3016 -

°C

Heating value DIN 51900-1

DIN 51900-2 - MJ/kg


Page | 12

1.4 Microalgae

Algae are photosynthetic microorganisms. (San Pedro, Gonzalez-Lopez, Acien, and

Molina-Grima, 2013). The habitat of most of the algae is saline water or freshwater.

Although some forms are found in different habitat including soils, and desert and even

snow (Suali and Sarbatly, 2012). Algae can be autotrophic or heterotrophic. Autotrophic

mode of cultivation of algae is better for oil production as compared to heterotrophic

mode because autotrophic mode does not need glucose or other nutrients as food source.

Moreover they also fix environmental carbon dioxide to glucose.

Renewable energy production from microalgae is not a new concept and has been carried

out since last two centuries. In 1957 Golueke et al., produced methane fuel from the

anaerobic digestion of microalgae for the first time. In 1957 and 1967, Oswald et al.,

conducted experiments and concluded that the average algal biomass productivity was

also much higher as compared to wheat. The most extensive research into the

development of biofuels from algae was performed by the National Renewable Energy

Laboratory (NREL) from 1978 to 1996 (Singh and Dhar, 2011). Microalgae can be used

for production of several types of renewable fuels such as biodiesel, methane, hydrogen,

ethanol etc. Algae biodiesel is being used as an alternative to fossil diesel with many

beneficial features which are not found in petroleum diesel (Mata, Martins, and Caetano,

2010).

1.5 Oil Productivity of Microalgae

The cost of biodiesel production remains a major barrier for commercial. It is mainly due

to the high supply cost of vegetable oils. Moreover another significant concern is the

inefficiency and unsustainability of these first and second generation biodiesel feed

stocks (Balat and Balat, 2010). Biodiesel from oil crops has been produced in increasing

amounts as an environment friendly alternative fuel but its production on large scale is

not sustainable. In contrast, third generation biodiesel feed stocks especially microalgae

have emerged as one of the most promising sources of lipid for biodiesel production

because microalgae can produce a higher yield. Moreover, they grow very rapidly,

resulting in higher biomass productivity and oil yield compared to other oil crops (San

Pedro et al., 2013). Microalgae with high oil content have the potential to produce an oil


Page | 13

yield that is up to 25 times higher than the yield of traditional biodiesel crops, such as oil

palm. Microalgae require only 0.1 m2 of land to produce 121,104 kg of biodiesel per

year. Due to this large production value, microalgae have been recognized as a

potentially best source for biodiesel production. Microalgae oil yield is strain-dependent

and it is generally much greater than other vegetable oil crops, as shown in Table 3. A

comparasion of biodiesel production efficiencies and land use of microalgae with other

vegetable oil crops is summarized in table 3. The oil contents are similar between seed

plants and microalgae but there are significant variations in the overall biomass

productivity and resulting oil yield (table 3). In terms of land use, palm oil got higher

position in the list among the other plant sources but microalgae shows much higher

biomass productivity and oil yield (Mata et al., 2010).

Table 3: Comparison of microalgae with other biodiesel feed stocks (Mata et al., 2010).

Plant source Oil content (% oil by wt. in

biomass)

Oil yield (l oil/ha/year)

Land use (m2 year/kg

biodiesel)

Biodiesel

productivity (kg biodiesel/ha/year)

Corn/Maize (Zea mays L.)

44 172 66 152

Hemp (Cannabis sativa L.)

33 363 31 321

Soybean (Glycine max L.)

18 636 18 562

Jatropha (Jatropha curcas L.)

28 741 15 656

Camelina (Camelina sativa L.)

42 915 12 809

Canola/rapeseed (Brassica napus L.)

41 974 12 862

Sunflower (Helianthus annuus L.)

40 1070 11 946

Castor (Ricinus communis)

48 1307 9 1156

Palm oil (Elaeis guineensis)

36 5366 2 4747

Microalgae (low oil content)

30 58,700 0.2 51,927

Microalgae (medium oil content)

50 97,800 0.1 86,515

Microalgae (high oil content)

70 136,900 0.1 121,104

The average lipid content of microalgae varies between 1 and 70% but under certain


Page | 14

conditions some species can reach 90% of dry weight (Y. Li, Horsman, Wang, Wu, and

Lan, 2008). Table 4 presents both lipid content and biomass productivities of different

marine and freshwater microalgae species, showing significant differences between the

various species. As shown in Table 4, oil content in microalgae can reach 75% by dry

weight but associated with low productivities (e.g. for Botryococcus braunii). Most

common algae including Chlorella, Crypthecodinium, Cylindrotheca, Dunaliella,

Isochrysis, Nannochloris, Nannochloropsis, Neochloris, Nitzschia, Phaeodactylum,

Porphyridium, Schizochytrium, Tetraselmis have oil levels between 20 and 50% but

higher productivities can be reached. During the selection of the most adequate species,

it is important to consider other factors as well, such as the ability of microalgae to

develop using the nutrients available or under specific environmental conditions.

Moreover, the composition of fatty acids of the different microalgae species is also

important, as they can have a significant effect on the characteristics of biodiesel

produced.

Table 4: Typical ranges of lipid content and productivity of selected marine and

freshwater microalgal species.

Microalgal species Lipid content

(%, w/w dry

weight)

Biomass

productivity

(g/L/day)

Habitat Reference

Ankistrodesmus sp. 28–40 - - (Ratha and Prasanna,

2012)

Botryococcus sp. 25.0–75.0 0.02 Fresh water (Malcata, 2011)

Chaetoceros calcitrans 14.6–39.8 0.04 Fresh water (Malcata, 2011)

Chaetoceros muelleri 33.6 0.07 - (Mata et al., 2010)

Chlamydomonas reihardtii 25 - - (Ratha and Prasanna,

2012)

sChlorella vulgaris 5.0–58.0 0.02-0.20 - (Malcata, 2011)

Chlorella emersonii 25.0–63.0 0.036-.041 Fresh water (Malcata, 2011)

Chlorella protothecoides 14.6–57.8 2.00-7.70 - (Mata et al., 2010)

Chlorella pyrenoidosa 2.0 2.90-3.64 - (Mata et al., 2010)

Chlorella sorokiniana 19.0–22.0 0.23-1.47 - (Mata et al., 2010)

Chlorella sp. 10.0–57.0 - - (Malcata, 2011)

Chlorella zofingiensis 79 - - (Ratha and Prasanna,

2012)

Chlorococcum littorale 34 - - (Ratha and Prasanna,

2012)


Page | 15

Chlorococcum sp. 19.3 0.28 Fresh water (Malcata, 2011)

Crypthecodinium cohnii 20 10 - (Ratha and Prasanna,

2012)

Cyclotella sp. 42 - - (Ratha and Prasanna,

2012)

Dunaliella primolecta 23.1 0.09 Fresh water (Malcata, 2011)

Dunaliella salina 6.0–25.0 0.22-0.34 (Mata et al., 2010)

Dunaliella sp. 17.5–67.0 - (Mata et al., 2010)

Dunaliella tertiolecta 16.7–71.0 0.12 (Mata et al., 2010)

Ellipsoidion sp. 27.4 0.17 Fresh water (Malcata, 2011)

Haematococcus pluvialis 25.0 0.05-0.06 Fresh water (Malcata, 2011)

Hantzschia sp. 66 - - (Ratha and Prasanna,

2012)

Isochrysis galbana 7.0–40.0 0.32-0.60 Sea water (Malcata, 2011)

Isochrysis sp. 7.1–33.0 0.08-0.17 - (Malcata, 2011)

Monallanthus salina 20 0.08 - (Ratha and Prasanna,

2012)

Nannochloris sp. 20.0–56.0 0.17-0.51 Sea water (Malcata, 2011)

Nannochloropsis oculata 22.7–29.7 0.37-0.48 Sea water (Malcata, 2011)

Nannochloropsis sp. 12.0–53.0 0.17-1.43 - Malcata, 2011)

Neochloris oleoabundans 29.0–65.0 - Sea water (Malcata, 2011)

Nitzschia sp. 28-50 - - (Ratha and Prasanna,

2012)

Pavlova lutheri 35.5 0.14 - (Mata et al., 2010)

Pavlova salina 30.9 0.16 Sea water (Malcata, 2011)

Phaeodactylum tricornutum 18.0–57.0 0.003-1.9 Sea water (Malcata, 2011)

Scenedesmus obliquus 11.0–55.0 0.04-0.94 Fresh water (Malcata, 2011)

Scenedesmus quadricauda 1.9–18.4 0.19 - (Malcata, 2011)

Scenedesmus sp. 19.6–21.1 0.03-2.1 - (Malcata, 2011)

Schizochytrium sp. 50-77 - - (Malcata, 2011)

Spirulina platensis 4.0–16.6 0.06-4.3 Sea water (Malcata, 2011)

Tetraselmis suecica 8.5–23.0 0.12–0.32 - (Mata et al., 2010)

Tetraselmis sp. 12.6–14.7 0.30 - (Varfolomeev and

Wasserman, 2011)


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1.6 Merits and Demerits of Microalgal Biodiesel

Production of biodiesel from microalgae has various advantages. Some of them are

following:

Microalgae are efficient biological system for converting solar energy to use in the

production of organic compounds.

Microalgae fix carbon dioxide in the atmosphere, facilitating the reduction of

atmospheric carbon dioxide levels. For production 1 kg of dry algal biomass about

1.8 kg of CO2 is needed.

Microalgae do not compete for land with crops used for food production, fodder and

other products. Moreover, the cultivation of microalgae does not require a large area

of land compared to other plant sources.

Microalgae can be grown in a number of environments that are unsuitable for

growing other crops, such as fresh, brackish or salt water or non-arable lands that are

unsuitable for conventional agriculture. They can also be grown on farms or in

bioreactors.

The most common microalgae have oil levels in the range of 20 to 50% by weight of

dry biomass, but higher oil productivities can be achieved depending on growth

parameters.

Microalgae commonly double their biomass within 24 h, but exponential growth

rates can result in a doubling of their biomass in periods as short as 3.5 h.

The costs of harvesting and transportation of microalgae are relatively low in

comparison with other biomass materials such as trees and crops.

They do not directly affect the human food supply chain. Therefore there is no food

versus fuel dispute.

Cultivation of microalgae does not require herbicides or pesticides.

Microalgae produce valuable co-products or by-products such as biopolymers,

proteins, carbohydrates and residual biomass, which may be used as feed or fertilizer.

Microalgal lipids are mostly neutral lipids with high degree of saturation, and their

accumulation in the microalgal cell at different stages of growth (depending on the

strain).


Page | 17

Beside various advantages there are some disadvantages of microalgal biodiesel these

disadvantages are following:

Difficult to find one kind of perfect microalgae from more than 20,000 kinds of

microalgae with high lipid content, that is easy to harvest and cost effective

There are only few of commercial microalgae production companies in the world.

Environmental impacts are uncertain caused by microalgae because less life cycle

assessment has been made.

Lack of governmental support.

1.7 Chemical Analysis of Biodiesel

During the transesterification process triacylglycerol is converted into fatty acid methyl

ester (FAMEs). During the reaction, intermediate monoacyl glycerol (MAG) and diacyl

glycerol (DAG) are also formed, small amounts of which can remain in the final

biodiesel product. Besides these, unreacted triacyl glycerol (TAG) as well as unseparated

glycerol, free fatty acids (FFA), residual alcohol, and catalyst can contaminate the final

product. These contaminants can lead to severe operational problems such as engine

deposits, filter clogging, or fuel deterioration. The analysis of various chemical

component of biodiesel are discussed below:

Methanol:Contamination of methanol in biodiesel is indirectly determined by flash

point. Flash point specifications for ASTM D6751 and EN 14214 are given found in

Tables 2 and 3, respectively. Contaminated biodieselwith methanol does not meet the

minimum flash point which is specified in both fuel standards. Inadequate purification of

biodiesel mayresults in methanol contamination.

Water: Water is one of maincauses of fuel contamination. During the production

processit may enter during water washing or may enter due to contact ofbiodiesel with

environmental humidity (Gerhard Knothe, 2005). Biodiesel contaminated with water can

cause various problems including corrosion of engine and other system components,

induces growth ofmicrobes, and biodiesel hydrolysis.Tables 1 (ASTM D6751) and 2 (EN

14214) show limits on water content in biodiesel.Water that contaminatebiodieselare two

types one is dissolved and other is free water. Dissolved water is determined by the Karl


Page | 18

Fisher titration method in EN 14214 according to EN ISO 12937 (Table 2). Free water is

determined by a centrifugation method (ASTM D2709) in ASTM D6751 (Table 1).

Residual Catalyst: Inadequate purification of biodiesel may results in residual catalyst

contamination. Sodium and potassium can be determined combine by test EN 14538.

Washing with hard water during purification step contaminate the biodiesel with calcium

and magnesium.Drying agents such as magnesium sulfate can also contaminate the

biodiesel. Both ASTM D6751 and EN 14214 have set limits for combined sodium and

potassium and combined calcium and magnesium contents(Tables 1 and 2). These metals

contamination cause various problems includinghigh ash production during combustion

(Gerhard Knothe, 2005).

Glycerol: Glycerol may be present in biodiesel due improper purification step. Presence

of glycerol also causevarious problemsincluding tank corrosion, clogging of fuel filter,

productionof harmful gasses during combustion(Knothe et al. 2005). Both biodiesel

standard organizationshave set limits foracceptable glycerol level in biodiesel (Tables 1

and 2).

Bound Glycerol: Bound glycerol (MAG + DAG + TAG) in biodiesel results from

incomplete transesterification reaction.Bound glycerol cause numerous problems

including carbon deposits during combustion, low temperature operability, high

kinematic viscosity. (Gerhard Knothe, 2005). Specification and limitation by ASTM for

bound glycerol is givenin table whereas in table 2 shows specification and limitation

byEuropean union standards for bound glycerol.

Free Fatty Acids: Biodieselhydrolyze to free fatty acids in the presence of water and

catalyst. The FFA in biodiesel affect important fuel properties such as oxidative stability,

kinematic viscosity and lubricity. Moreover they also take part in soap formation. acid

value shows the presence of FFA and acid value (AV) is determined by ASTM D664

method. Tables 1 and 2 show allowable limits of AV in ASTM D6751 and EN 14214

respectively.

Minor Components: Beside water, glycerol bound glycerol residual catalyst biodiesel

may contain some other minor components includingchlorophyll, tocopherols,

phospholipids, steryl glucosides, fat soluble vitamins, and hydrocarbons. The quantities

of minor components vary from source to source of biodiesel and purification stepafter


Page | 19

transesterification. The degree of pre-processing prior to transesterificationwhich

includes refining, bleaching, deodorization and degumming also affect the presence

concentration of minor components. Most of the minor components have positive effect

on biodieselfor example tocopherols act asantioxidants and chlorophylls are sensitizers

for photo-oxidation (Dunn, 2005; Fröhlich and Schober, 2007; Martin Mittelbach and

Schober, 2003; Moser, 2008; Tang, Wang, Salley, and Ng, 2008) whereas steryl

glucosides in biodiesel causes various problems including low temperature operability at

temperatures above cloud point, engine failure due to fuel filter plugging and

precipitation. Both ASTM D6751 or EN 14214do not show the allowable level of steryl

glycoside. But recently ASTM presented a new analytical method to determine the

presence of precipitates that form above the cloud point of the fuel. This test is termed as

“cold soak filtration” test. Gas chromatography or High performance liquid

chromatography can be used to determine steryl glycosides(Paolo Bondioli, Cortesi, and

Mariani, 2008).


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1.8 Mass Spectrometry

Mass spectrometry is a powerful technique which generates ions, separates and detects

those ions according to their mass-to-charge ratio (m/z). Sample can be ionized by

electric fields, thermally or by energetic ions, electrons or photons. Because of its

accuracy and sensitivity, mass spectrometry is extensively used in different fields such as

in biotechnology, pharmacology, metabolomics, proteomics , pharmaceutical chemistry

environmental chemistryetc.

1.8.1 Ionization Techniques

Several ionization methods are being used depending on the requirements, nature and

type of samples (Henderson and McIndoe, 2005). Frequently used ionization techniques

are discussed below and summarized in table 5.

1.8.1.1 Electron Impact (EI)

Electron impact is the traditional and the most common method of ionization. The

sample for analysis is introduced into the ion source, either through a solids inlet or

through a gas chromatography column. It is essential that the sample enters into the ion

source must be in the gaseous state and the ability to heat the source and solids probe are

important for successful sample analysis. A beam of electrons produced by a heated

filament of either Tungsten or Rhenium collides with the sample gas molecules, removes

an electron and produces a positively charged ion corresponding to the relative molecular

mass of the sample being analyzed. Electron impact is an energetic ionization technique

and also produces fragment ions which are smaller parts of the original molecule

(Hoffmann and Stroobant, 2007).

1.8.1.2 Chemical Ionization (CI)

Chemical ionization is a soft ionization technique and yields less fragmentation, and

produces a simpler spectrum. In chemical ionization, ions are produced through the

collision of the analyte with ions of a reagent gas that are present in the ion source. Some

common reagent gases include methane, ammonia and isobutene are mostly used. Inside

the ion source, the reagent gas is present in large excess compared to the analyte.

Electrons entering the source will preferentially ionize the reagent gas. The resultant

collisions with other reagent gas molecules will create ionization plasma. Positive and


Page | 21

negative ions of the analyte are formed by reactions with this plasma(Dougherty, 1981;

Ghaderi, Kulkarni, Ledford, Wilkins, and Gross, 1981; Kontsas and Pekari, 2003;

Lacorte and Guillamon, 2008; Rivera-Rodríguez, Rodríguez-Estrella, Ellington, and

Evans, 2007).

1.8.1.3 Fast Atom Bombardment (FAB)

This method is also known as liquid secondary ion mass spectrometry. The material to be

analyzed is mixed with a matrix and is bombarded under vacuum with a high energy

(4000 to 10,000 electron volts) beam of atoms. The atoms are typically from an inert gas

such as argon or xenon. Common matrices include glycerol, thioglycerol, 3-nitrobenzyl

alcohol (3-NBA), 18-crown-6 ether, 2-nitrophenyloctyl ether, sulfolane, diethanolamine,

and triethanolamine(Barber, Bordoli, Sedgwick, and Tyler, 1981; Tomer, 1989; W,

1984).

1.8.1.4 Matrix Assisted Laser Desorption/Ionization (MALDI)

Matrix-assisted laser desorption/ ionization (MALDI) is a soft ionization technique used

for the analysis of biomolecules (biopolymers such as DNA, proteins, peptides and

sugars) and large organic molecules (such as polymers, dendrimers and other

macromolecules), which tend to be fragile and fragment when ionized by more

conventional ionization methods. The most commonly used matrixes are 3,5-dimethoxy-

4-hydroxycinnamic acid (sinapinic acid), α-cyano-4-hydroxycinnamic acid (HCCA) and

2,5-dihydroxybenzoic acid (DHB)(Knochenmuss, 2006; Strupat, Karas, and Hillenkamp,

1991).

1.8.1.5 Electrospray Ionization (ESI)

In Electrospray ionization (ESI), the sample is infused under atmospheric pressure into

the ionization source through a thin needle after dissolving in a preferably polar solvent.

During the constant introduction of the liquid sample along with a warm neutral gas

(usually nitrogen) called nebulizing gas, a very high electrical potential is applied at the

needle (3-8 kV), resulting in the formation of a spray richer in highly charged droplets

(i.e., nebulization). Under these conditions, solvent continuously evaporates from the

droplets leading to the continuous reduction in droplet size. Ultimately, the repulsive

forces such as the columbic forces, between the ions on the surface of the shrinking


Page | 22

droplets become exceedingly high eventually exceeding the surface tension of the

solvent, resulting in desorption of ions into the gas phase(Ho et al., 2003).

Table 5: Summary of the different properties of frequently used mass spectrometric

ionization techniques.

Ionization

technique

Nature of

analytes

Sample

introduction

Mass range

(amu)

Briefs description

EI Volatile and

thermally stable

GC, solid and

liquid probe

<1000 Hard ionization

technique and produces

fragment ions

CI Volatile and

thermally stable

GC, solid and

liquid probe

<1000 Soft ionization method

and produces molecular

ions

FAB Polar

compounds

LC and direct

injection

<1500 Soft ionization method

and require matrix

MALDI Biomolecules Sample is

cocrystalized

with a matrix

>500,000 Very soft ionization

method and require

matrix

ESI Organic and

inorganic

compounds

LC and liquid

probe

>100,000 Very soft ionization

method and produces

multiply charge ions

1.8.2 Mass Analyzers

A mass analyzer is the component of the mass spectrometer that separates ionized

masses on the basis of charge-to-mass ratios and followed by detection through detector.

There are six general types of mass analyzers that can be used for the separation of ions

in mass spectrometry. These analyzers differ in terms of mass range, size, resolutionand

priceCharacteristics of different mass analyzers are summarized in table 6 and brief

description of each analyzer is given below:

1.8.2.1 Quadrupole

Quadrupole analyzers have 4 parallel rods.arranged in a square formation. At any given

time of analysis, one diagonal pair of rods is positively charged and the other diagonal

pair is negatively charged. These charges alternate at a set frequency such that balanced


Page | 23

attraction and repulsion of the ion of interest maintains a stable flight path between the

pairs of rods. The amount of positive or negative charge and the frequency at which the

charges alternate is optimized for each analyte and can be rapidly changed throughout

the duration of the analysis. Quadrupole analyzers have a limited m/z range, high

sensitivity and mass accuracy, but low percentage of ion transmission (Shrader, 2014).

1.8.2.2 Time-of-Flight

In time-of-flight (TOF), an ion's mass-to-charge ratio is determined via a time

measurement. Ions are accelerated by an electric field of known strength. The time that it

subsequently takes for the particle to reach a detector at a known distance is measured.

This time will depend on the mass-to-charge ratio of the particle i.e. heavier particles

reach the detector with lower speeds. From this time and the known experimental

parameters, the mass-to-charge ratio of the ion is estimated(Hoffmann et al., 1996).

1.8.2.3 Magnetic Sector

In magnetic sector analyzers ions are accelerated through a flight tube, where the ions are

separated by charge-to-mass ratio, similar to time-of-flight (TOF) analyzer. The

difference between magnetic sector and TOF is that a magnetic field is used to separate

the ions. As moving charges enter into a magnetic field, the charge is deflected to a

circular motion of a unique radius in a direction perpendicular to the applied magnetic

field. Ions in the magnetic field experience two equal forces; force due to the magnetic

field and centripetal force. Basically the ions of a certain m/z value will have a unique

path radius which can be determined if both magnetic field magnitude B, and voltage

difference V for region of acceleration are held constant. When similar ions pass through

the magnetic field, they all will be deflected to the same degree and will all follow the

same trajectory path. Those ions which are not selected by V and B values will collide

with either side of the flight tube wall or will not pass through the slit to the detector.

(Shrader, 2014).

1.8.2.4 Quadrupole Ion Trap

This analyzer employs similar principles as the quadrupole analyzer mentioned but it

uses an electric field for the separation of the ions by mass-to-charge ratio. The analyzer

is made with a ring electrode of a specific voltage and grounded end cap electrodes. The

ions enter the area between the electrodes through one of the end caps. After entrance,


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the electric field due to the electrodes in the cavity causes the ions of certain m/z values

to circular movement in the space. As the radio frequency voltage increases, heavier

mass ion orbits become more stabilized and the light mass ions become less stabilized,

causing them to collide with the wall, and eliminating the possibility of traveling to and

being detected by the detector. The quadrupole ion trap usually runs a mass selective

ejection, where selectively it ejects the trapped ions in order of increasing mass by

gradually increasing the applied radio frequency voltage (Hoffmann et al., 1996).

1.8.2.5 Ion Cyclotron Resonance (ICR)

ICR is an ion trap that uses a magnetic field in order to trap ions inside of it into an orbit.

In this analyzer, there is no separation that occurs rather all the ions of a particular range

are trapped inside, and an applied external electric field helps to generate a signal. When

a moving charge enters into a magnetic field, it experiences a centripetal force making

the ion orbit. Again the force on the ion due to the magnetic field is equal to the

centripetal force on the ion. Frequency of the orbit depends on mass-to-charge ratio of

the ions, not the velocity. If the magnetic field is held constant, the charge-to-mass ratio

of each ion can be determined by measuring the angular velocity. Charges of opposite

signs have the same angular velocity, the only difference is that they orbit in the opposite

direction. To generate an electric signal from the trapped ions, a range of electric field is

applied to the ion trap. When the angular velocity in the electric field matches the

angular velocity of a certain ion, the ion absorbs energy making the velocity and orbiting

radius of the ion increase. In this high energy orbit, as the ion oscillates between two

plates, electrons accumulate at one of the plates over the other inducing an oscillating

current, or current image. The current is directly proportional to the number of ions in the

cell at a certain frequency. In a Fourier Transform ICR, all of the ions within the cell are

excited simultaneously so that the current image is coupled with the image of all of the

individual ion frequencies (Shrader, 2014).

1.8.2.6 Orbitrap

The orbitrap is the newest type of mass analyzer and was introduced commercially in the

few past years. It is basically a trap-type device, because ions are trapped in the orbitrap,

then they travel in a circular motion before being selectively ejected according to their

m/z values. Similar scan types can be performed by ion traps but the orbitrap provides


Page | 25

very high resolution and a high mass accuracy comparable with QqTOF instruments (Hu

et al., 2005).

Table 6: Comparison of various mass analyzers.

Type Mass range

(m/z)

Resolution

m/∆m

Mass accuracy

(ppm)

Quadrupole mass filter 1,000 - 4,000 500 -2,000 100

Time-of-flight >1,000,000 500 -35,000 200

Magnetic sector 5,000 – 10,000 500 -100,000 10

Quadrupole ion trap 2,000 - 6,000 1000 -5,000 100

Ion Cyclotron Resonance 5,000 - 10,000 >10,000,000 <2

Orbitrap 2,000 - 4,000 >200,000 <5

1.8.3 Tandem Mass Spectrometry (MS/MS)

Tandem mass spectrometry is also known as MS/MS or MS2. The latter terminology

elegantly allows for the expansion to multi-stage experimental setups, e.g., MS3, MS

4 or

generally MSn. It involves multiple steps of mass spectrometry selection, with some form

of fragmentation occurring in between the stages. Tandem mass spectrometry involves

the two stages of mass spectrometry and fragmentation of molecules takes place in

between these stages. There are two major types of instruments which can perform

MS/MS experiments. In the first type of instruments, two mass analyzers are assembled

in tandem like two magnetic sectors, two quadrupoles, or one magnetic sector and

electric sector or one magnetic sector one quadrupole etc. The second type of

instruments consists of analyzers which have the capability to store ions like FTICR and

ion trap (Prasain, 2012).

1.8.3.1 Types of Tandem Mass Spectrometry

There are two types of tandem mass spectrometry. Brief description of each is given in

the following:

Tandem in Space: In tandem mass spectrometry in space, there are physically separated

and distinct elements. These elements can be sectors, transmission quadrupole, or time of

http://en.wikipedia.org/wiki/Mass_spectrometry

http://en.wikipedia.org/wiki/Sector_mass_spectrometer

http://en.wikipedia.org/wiki/Quadrupole_mass_analyzer

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flight. Although, there is a physical connection between the elements to maintain high

vacuum. Triple quadrupole mass spectrometer(QqQ), quadrupole time of flight mass

spectrometer (QTOF or QqTOF) and four sectors mass spectrometer (BEBE) are

examples of tandem mass spectrometry in space.

Tandem in Time: An ion trap mass spectrometer is an example of a tandem mass

spectrometry in time instrument. By doing tandem mass spectrometry in time, the

separation is accomplished with ions trapped in the same place, with multiple separation

steps taking place over time. A quadrupole ion trap or FTMS instrument can be used for

such an analysis. Trapping instruments can perform multiple steps of analysis, which is

sometimes referred as MSn (MS to the n). Often the number of steps, n, is not indicated,

but occasionally the value is specified; for example MS3 indicates three stages of

separation. Tandem in time MS instruments typically collect all of the information from

a precursor ion scan and a parent ion scan of the entire spectrum (Prasain, 2012).

1.8.3.2 Methods for the Activation of Ions for MS/MS Analysis

1. Collision Induced Dissociation (CID): It is also known as collisionally activated

dissociation (CAD). It is a technique to induce fragment of molecular ions in the gas

phase. The molecular ions are usually accelerated by some electrical potential to high

kinetic energy and then allowed to collide with neutral molecules (often helium,

nitrogen or argon). In the collision some of the kinetic energy is converted into internal

energy which results in bond breakage and the fragmentation of the molecular ion into

smaller fragments. These fragment ions can then be analyzed by a tandem mass

spectrometry. The fragment ions produced by CID are used for several purposes. Partial

or complete structural determination can be achieved. In some cases identity can be

established based on previous knowledge without determining structure. Another use is

in simply achieving more sensitive and specific detection. By detecting a unique

fragment ion, the precursor ion can be detected in the presence of other ions of the mass-

to-charge ratio, reducing the background and increasing the limit of detection.

2. Electron Capture and Transfer Methods

(a) Electron Capture Dissociation (ECD): It involves the direct introduction of low

energy electrons to trapped gas phase ions.

http://en.wikipedia.org/wiki/Vacuum



http://en.wikipedia.org/wiki/Triple_quadrupole_mass_spectrometer

http://en.wikipedia.org/wiki/Fragmentation_(chemistry)

http://en.wikipedia.org/wiki/Ion

http://en.wikipedia.org/wiki/Accelerate

http://en.wikipedia.org/wiki/Electrical_potential

http://en.wikipedia.org/wiki/Kinetic_energy

http://en.wikipedia.org/wiki/Helium

http://en.wikipedia.org/wiki/Nitrogen

http://en.wikipedia.org/wiki/Argon

http://en.wikipedia.org/wiki/Internal_energy



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(b) Electron Transfer Dissociation (ETD): It induces fragmentation of cations by

transferring electrons to them.

(c) Negative Electron Transfer Dissociation (NETD): Fragmentation occurs with a

deprotonated species and an electron is transferred from the specie to a cationic

reagent in negative electron transfer dissociation.

(d) Electron Detachment Dissociation (EDD): It serves as a negative counter mode to

electron capture dissociation. Negatively charged ions are activated by irradiation

with electrons of moderate kinetic energy. The result is ejection of electrons from the

parent ionic molecule, which causes dissociation via recombination.

3. Photodissociation: The energy required for dissociation can be added by photon

absorption, resulting in ion photodissociation but can lead to excessive fragmentation of

biomolecules.

(a) Infrared Multiphoton Dissociation: Infrared photons will heat the ions and cause

dissociation if enough of them are absorbed. This process is called infrared

multiphoton dissociation (IRMPD) and is often accomplished with a carbon and an

ion trapping mass spectrometer such as a FTMS.

(b) Blackbody Infrared Radiative Dissociation: Blackbody radiation can be used for

photodissociation in a technique known as blackbody infrared radioactive

dissociation (BIRD).In the BIRD method, the vacuum chamber ofmass spectrometer

is heated to create infrared radiation. BIRD uses the light from black body radiation

to thermally (vibrationally) excite the ions until a bond breaks. This is similar to

dissociation with the exception of the source of radiation. This technique is most

often used with Fourier transform ion cyclotron resonance mass spectrometers

(Prasain, 2012).

1.8.3.3 Scan Modes

1. Product Ion Scan: It provides qualitative structural information. The first mass

analyzer chooses a particular precursor ion, allowed to fragment and second mass

analyzer scans the product ions through a specific m/z range. It can collect MS/MS


Page | 28

spectra for complex mixtures and can be used with chromatographic techniques for

examples HPLC-Ion Trap and HPLC-Q-TOF.

2. Precursor Ion Scan: It involves scanning the first analyzer for precursor masses and

the product ion is selected in the second analyzer.

3. Constant Neutral Loss Scan: This relatively complex scan is used for the

identification of a particular class of compounds having same neutral losses. Both

analyzers are scanned simultaneously with a mass offset associated with the mass of the

particular neutral in order to permit parent and fragment ion pairs which have a specific

m/z shift to be analyzed. (de Hoffmann, 1996).

4. Selected Reaction Monitoring (SRM or MRM): In selected reaction monitoring

(SRM) or multiple reactions monitoring (MRM), both Q1 and Q3 are set to a selected

mass, allowing only a distinct fragment ion from a certain precursor ion to be detected.

This method results in increased sensitivity. If Q1 and/or Q3 is set to more than a single

mass, this configuration is called multiple reaction monitoring. .


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1.9 Gas Chromatography-Mass Spectrometry: A

Promising Tool for Oil Analysis

Gas chromatographic-mass spectrometry (GC-MS) is a very powerful and ubiquitous

analytical technique. This hybrid instrument replaces the traditional thermal conductivity

detector (TCD) or flame ionization chromatographic detector (FID) with a very sensitive

and information-rich mass spectrometer (MS). Not only can a GC-MS separate the

volatile components of complex mixtures, but it can also record a mass spectrum of each

component. This hybrid instrument provides two separate dimensions of information

about the components in the sample, GC retention times and electron ionization (EI)

mass spectra. GC retention time is related to specific chemical properties of the

molecules (e.g. volatility, polarity, presence of specific functional groups) while

molecular weight (derived from the mass spectrum) is indicative of atomic composition.

(Li, Ren, Sun, and Huang, 2013)

There are a number of different possible GC/MS configurations, but all share common

types of components. The sample is introduced through a gas chromatograph (GC).

Therefore it does not involve the sample purification. A gas chromatograph cosists of

carrier gas source, control valves, temperature control oven and tubing to connect the

injector to the column and out to the mass spectrometer interface or transfer line. A

column packed with support and coated with a stationary phase for the separation of

components. The interface module transfers the separated compounds to the mass

spectrometer‟s ionization source without remixing. The mass spectrometer system is

made up of the ionization source, focusing lens, mass analyzer, ion detector, and

multistage pumping. A data control system provides mass selection, lens, detector

control, data processing and interfacing to the GC and mass spectrometer.

The fatty acids of animal and plant origin are commonly consist of even numbered

chains of 16 to 22 carbon atoms with zero to six double bonds of the cis configuration.

Methylene group is also present in between multi double bond systems. Although there

are some exceptions including odd chain fatty acids and even numbered fatty acids with

up to nearly a hundred carbon atoms. Double bonds can be of the trans configuration and

acetylenic and allenic bonds may occur. Moreover, there many other structural features,

including branch points, rings, oxygenated functions, and many more.


Page | 30

Therefore, it is essential to have simple rapid methods for determination of fatty acid

structures and for isolation of pure components of mixtures for further analysis. In

particular, new methods involving gas chromatography-mass spectrometry (GC-MS),

GC linked to Fourier-transform infrared spectroscopy (FTIR), and silver ion and

reversed-phase high-performance liquid chromatography (HPLC) are available, amongst

others. GC-MS with electron-impact ionization is the most useful technique.

Straightforward derivatization procedures are required that utilize readily available

reagents and have simple glassware requirements. A feature of particular importance

with GC-MS is that it is rarely necessary to isolate components in a pure form, as may be

required by other spectroscopic methods (e.g. NMR spectroscopy) or by chemical

degradative procedures.

Fatty acids are usually analysed by GC-MS as methyl ester derivatives. Their molecular

weight and retention times are useful analytical parameters, some limited structural

information may be available, and indeed definitive spectra can be obtained often with

branched-chain fatty acids or those with additional oxygenated functional groups. But

their mass spectra may not always contain ions indicative of key structural features; the

positions of double bonds in the aliphatic chain, for example, can only rarely be

determined clearly.

In the last few years, some very interesting papers have appeared dealing with

acetonitrile-chemical-reaction tandem mass spectrometry in the gas phase for locating

double bonds in fatty acid methyl esters. Mass spectrometry with atmospheric-pressure

chemical ionization (APCI) in conjunction with liquid chromatography has also been

used to characterize long chain fatty acids, and many research articles in which

electrospray ionization is used for the purpose are appearing in significant numbers.

31

2 CHAPTER 2:

OIL CONTENTS SCREENING OF

MICROALGAL ISOLATES FROM

SOUTHERN PAKISTAN THROUGH

GC-MS FOR BIODIESEL

PRODUCTION

Chapter 2: Oil Contents Screening of Microalgal Isolates from Southern Pakistan

Page | 32

2.1 Background

Microalgae are one of the best sourcefor biodiesel production becausethey grow rapidly

and have ability to accumulate high lipids content in the form of triacylglycerides

(TAG). These microorganisms are mostly photoautotrophic. They may be eukaryotic

prokaryotic species. Photoautotrophic microalgae convert water, carbon dioxide and

minerals to biomass, but some species also grow heterotrophically. Prokaryotic

microalgae includes cyanobacteria while eukaryotic microalgae includes the nine phyla

Rhodophyta, Chlorophyta, Chlorarachniophyta, Glaucophyta, Euglenophyta,

Cryptophyta, Heterokontophyta, Haptophyta and Dinophyta. There are about 50,000

species,out of them two third have been explored and identified.Various algal research

institutes of the world have large collection these microalgae. For example, the

University of Coimbra (Portugal) has the largest collection of freshwater algae about

4000 strains and 1000 species of algae.Göttingen University (Germany) has the culture

collection of 2213 strains and 1273 species of both freshwater and marine algae.

University of Texas (USA) hasmicroalgal collection of 2300 strains of freshwater

species. The National Institute for Environmental Studies (Japan)has maintained 2150

strains with about 700 species of both freshwater and marine algae (Bahadur, Boocock,

and Konar, 1995). The Australian National Algae Culture Collection (Australia)have

about 1000 microalgal strains.These algae collections are maintained for pharmaceutical,

food, energy, industrial products and other many purposes. Only few microalgal strains

werecultivated and investigated for chemical analysis. Microalgal strains are getting

interest for biodiesel production but the cost of biodieselproduction of microalgaeis

higher than from other feedstock. Therefore, there is a need to improve steps of biodiesel

production from microalgae. It includes selection of high lipid producing which can

produce right kind of lipid under native environment condition by utilizing low cost

nutrients.Efficient cultivation of microalgae and suitable location for cultivation of

microalgae are alsoimportant factors for reducing the cost. Moreover harvesting of

microalgaeand oil extraction from biomass of microalgae should be simple and cost

effected.

Pakistan possesses unique geological, geographical and atmospheric zones which

support biodiversity. Algal flora is also abundant in Pakistan due to diverse water and


Page | 33

rich saline land habitats. An important need is to explore this native floral wealth for

biodiesel production.

2.2 Microalgae Screening Strategy to Explore Potential

for Biodiesel Production

The best strategy to explore the microalgae for biodiesel production is isolation and

screening of large number of native microalgal strains for biodiesel production. Figure 3

shows step by step procedure forscreeningof microalgaehaving potential for biodiesel

production for commercial scale

2.2.1 Sampling

Habitat of microalgae is usuallywater, rocks and soil, but they can also grow on and in

other organisms. Microalgae can be found and collected not only in general aquatic

ecosystems such as lakes, rivers and the oceans, but also in extreme environments such

as volcanic waters and salt waters. It is better to collect sample of local microalgae

species as they have a competitive advantage under the local geographical, climatic and

ecological conditions. It is reported that microalgal samples collected from adverse

conditions provide a higher chance of isolating a microalgae with high lipid content.

Tidal rock pools, bays and rivers are examples of these environments. These conditions

favor fast growing microalgae with greater survival abilities which includes

accumulation of high lipid content.

2.2.2 Cultivation and Biomass Production

For biomass production microalgae are cultured. Microalgae are commonly grown at

laboratory scale by using culturing flasks, fermenter and hanging bags. Growth

conditions are well defined during culturing. A standard protocol provide allow direct

comparisons between strains in terms of growth rate and lipid productivity. In standard

protocol pure microalgal strains of fresh water are cultured in bolds basal

medium(samples) andF/2 medium (seawater samples) till stationary growth phase. 5 mL

of this culture is then inoculated to 20 mL bolds basal medium or F/2 medium tostart

growth. The growth of microalgal culture is monitored by cell counting for seven

days.Medium is replaced with nutrient free water after seven days. Nutrient starvation is

conducted for 2 days of cultivation to test the potential for triacylglerol accumulation.


Page | 34

Microalgal biomass is harvestedfor lipid content analysis at the end of the experiment.

This method is useful for screening of growth and lipid producing capability of

microalgae which ultimately leads to selection of potentially useful microlgal strain for

biodiesel production.

2.2.3 Isolation of Pure Cultures

Isolation is a required process to obtain pure microalgal cultures and it is the first step

towards the selection of microalgal strains having potential for biodiesel production at

commercial level. There are various isolation techniques which include micropipetting

under a microscope. Single cell isolation from the original axenic microalgae sample is

time consuming. Moreover it requires sterilized cultivation media and as well as

equipments, but it results a pure culture which can be easily identified. Another is the

cell dilution technique, followed by cultivation in liquid media or agar plates. one of

isolation method is nutrients enrichment approach which includes the adding of nutrients

which are particular for growth ofsome microalgae strains. Nitrogen and phosphate are

the most important nutrient sources for microalgal growth. Some group of microalgae

require minerals for their growth, for example diatoms(a group of microalgae) require

silicon. Soil water extract is also a good source of nutrients for microalgal growth

because preparation of this medium is easy.Moreover it also fulfills nutrient intake of

most of the microalgal strains. Flow cytometry is an automated single cell isolation

method which can also be used for cell sorting (Eltgroth, Watwood, and Wolfe, 2005).

The technique is effectivelybeing used for microalgal cell sorting from water with many

different algae strains. Thistechnique is based on chlorophyll autofluorescence (CAF)

and green autofluorescence (GAF) to discriminate algal species of various groups such

as diatoms, phytoplankton anddinoflagellates etc. There are several advantages of

automatic isolation techniques as compared to traditional methods but single cell

isolation by using glass capillary is quiet a very effective method and can be used for

anextensive range of samples. Moreover it is a cost effective cost effective method as

compared to automated technique.

In contrast to many agricultural crops, selection of potential microalgal strains and

cultivating of the microalgal strains at domestic level is still in its beginning.

Moreoverthe technology to cultivate microalgal strains with high and right kind of lipid

content is being developed. Every microalgal strain needs careful selection and


Page | 35

optimization of biomass and lipid productivity and high quality biodiesel production with

required performance properties.

2.2.4 Lipid Determination

Lipid determination is important step for identification of suitable strains for biodiesel

production. It includes qualitative and quantitative analysis. Bligh and Dyer is

gravimetric method for extraction of lipid from microlgal biomass. It is the most

common method which based onextraction of lipids with solvents. (E. G. Bligh and

Dyer, 1959).

2.2.5 Profiling of Lipid Components

Lipid components from extracted oil are separated and their profiling

requirevarioustechniques to satisfy quality criteria of produced biodiesel. These

techniquesincludes thin layer chromatography (TLC), gas chromatography-flame

ionization detector (GC-FID), gas chromatography-mass spectroscopy (GC/MS) and

high pressure liquid chromatography (HPLC) etc.(Eltgroth et al., 2005).

2.2.6 Optimization of Lipid and Biomass Productivity for Commercial Scale

Biodiesel Production

After selection of the best microalgal strains among all isolated microalgal strains from

native environment having potential for biodiesel production in autotrophic condition the

next step is to optimize parameters for growth rate, lipid productivity, harvesting step,

drying step and oil extraction step for commercial step. Most of the parameters which

were optimized for laboratory scale can be move towards large scale cultivation.These

parameters include salinity, nutrient composition, pH and cell density can be controlled

to certainlevel, but other parameters which includes such as temperature, irradiation of

light and the co-cultivation of other microorganisms are very difficult to control under

large scale conditions. Environmental conditions can affect microalgal biomass

productivity, lipid contentand compositions of lipid. For instance, a preliminary study of

Chlorella sp.at showed that high biomass could be achieved by increasing the

concentration of nitrogen and lipid productivity could be achieved by decreasing

nitrogen concetration. Similarlyanother study showed that Botryococcus brauniiis

dependent on light, temperature, salinity, nutrient quantity and composition.


Page | 36

There are two most popular cultivation systems for microalgal biomass and lipid

production at larger scale. one is open raceway ponds and the other is closed

photobioreactors. Open pond systems are much more commonly used cultivation

systems due to minimizing the cost of microalgal farming.But it allow the risk of

attracting competing microalgae, grazers, viruses and other microorganism.closed

photobioreactors prevent from these contaminations but the cost of microalgal farming is

too high which is one of main priorities of microalgal farming at commercial scale for

biodiesel production. Both cultivation systems require optimization of various

parameters that fulfillthe criteria of high level cultivation at commercial scale.

Figure 3:Microalgae screening strategy to explore potential for biodiesel production.


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2.3 Experimental

2.3.1 Standards and Chemicals

FAME standards (RM-5 and Rapeseed mix) and BF3/methanol (derivitizing reagent)

were purchased from Sigma Aldrich (USA). Sodium hydroxide was purchased from Uni-

Chem (England). Sulphuric acid (conc.) was purchased from Merck (Germany).

Chemicals for algal media were purchased from BioM Laboratories (Cerritos, USA).

2.3.2 Microalgae Collection, Isolation and Identification

Microalgae were collected from both marine as well as fresh water habitats. Fresh water

samples were collected from different places of Karachi (southern Pakistan), including

Liyari river, river Indus and Kalari lake, whereas marine samples were collected from

Hawksbay, Paradise Point, Buleji and Korangi creek.

All samples were collected during June 2008 to May 2009. Various physical parameters

were recorded at the collection spots, including pH, temperature and salinity of water.

Moreover, the season of collection and the habitat of microalgae were also taken into

account. Samples were collected in a number of falcon tubes containing different types

of sterilized media.

Microalgae were grown in 250 mL Erlenmeyer flasks containing Bold‟s basal medium

for fresh water microalgae, whereas Guillard's f2 medium and enriched seawater medium

were used for marine samples. The media recipe of Bold‟s basal medium and Guillard's

f2 medium is given in table 7 and 8, respectively. Media were evenly distributed in flasks

and autoclaved for inoculation of the microalgal seed cultures. Flasks were placed in a

growth chamber assembled with 18 watt florescent light. Inoculated flasks were aerated

with flow rate of 5 L per min. Microalgae were isolated by using micromanipulation,

serial dilution and streak plating methods. Compound microscope (BX60; Olympus

Corp., LakeSuccess, NY) was used for preliminary observation, while Nikon TE-2000E

microscope (Melville, NY) was used for detailed examination and imaging. Microalgal

species were identified by the Ms. Noureen Zehra (Taxanomist) using previous reported

literature.


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Table 7:Media composition of Bold‟s Basal medium.

Nutrients Amount (g/L)

NaNO3 0.2500

MgSO4.7H2O 0.0750

NaCl 0.0250

K2HPO4 0.0750

KH2PO4 0.0175

CaCl2.2H2O 0.0250

ZnSO4.7H2O 0.0088

MnCl2.4H2O 0.0014

MoO3 0.0007*

CuSO4.5H2O 0.0015

Co(NO3)2.6H2O 0.0005*

H3BO3 0.0114

EDTA 0.0250

KOH 0.0155

FeSO4.7H2O 0.0049

H2SO4 (Conc.) 1 Ml

*These nutrients were added through serial dilution.


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Table 8: Media composition of Guillard f/2 medium.

Nutrients Amount (g/L)

NaNO3 0.075

NaH2PO4.2H2O 0.00565

Na2 EDTA 0.0041

FeCl3.6H2O 0.0031

CoCl2.6H2O 0.00001*

ZnSO4.7H2O 0.000022*

MnCl2.4H2O 0.00018*

Na2MoO4.2H2O 0.000006*

CuSO4.5H2O 0.00001*

Cyanocobalamin 0.0000005*

Thiamine HCl 0.0001*

Biotin 0.0000005*

*These nutrients were added through serial dilution.

2.3.3 Large Scale Cultivation of Miroalgae

Isolated microalgae were grown autotrophically in 5 L fermenter (Biostat Q, B. Braun,

and Germany) for oil extraction. Actively growing starter cultures (100 mL) were

inoculated into the fermenter, containing 5 L of autoclaved media. Cultures were grown

under continuous illumination of florescent light at 27±0.5 °C and aerated with air. The

cell count of cultures was monitored daily using a haemocytometer (HBG-Germany).

Microalgae were harvested for chemical analysis when the cultures attained the late-

logarithmic phase. Culture medium was centrifuged at 4,000 rpm at 20 °C for 10 min in

a centrifuge machine (J-6 MI centrifuge, Beckman Coulter, USA). Pellets were then

washed twice with 0.5 M ammonium formate to remove salt. The wet cell mass was

frozen overnight at −70 °C and then lyophilized in a freeze drier (Christ Alpha 1–2 LD

Plus, Germany) to measure cell dry weight and total lipid content.

2.3.4 Lipid Extraction and Derivitization of Oilgae

Lipids were extracted according to Bligh and Dyer protocol (E. G. D. Bligh, W.J. ,

1959). The weight of lipids was measured after evaporating the solvent on a rotary


Page | 40

evaporator (N-1000, Eyela, Japan). Percent lipid content was calculated by dividing the

weight of these oils by dry weight of microalgae.

2.3.5 Characterization of Biodiesel

Saponification and iodine values were determined according to the method of Gopinath

et al. (Gopinath, Puhan, and Nagarajan, 2009). Following parameters were used to

calculate saponification and iodine value. Saponification value (SV) = 268–(0.418 X P)–

(1.30 X S)–(0.695 X O)–(0.77 x L)–(0.847 X LL) Iodine Value (IV) = 35.9–(0.212 X

P)+(0.660 X S)+(0.448 X O) +(1.23 X L)+(1.73 X LL). Higher heating values of

biodiesels were calculated according to Ayhan Demirbas model (A. Demirbas, 1998).

Higher heating value (HHV) = 49.43–(0.015 x IV)–(0.041 X SV) where, P = palmitic, S

= stearic, O = oleic, L = linoleic and LL = linolenic. Cetane number, kinematic viscosity

and density of biodiesel were calculated from the FAMEs composition of every oil

according to the protocol of Ramírez-Verduzco et al. (Ramirez-Verduzco, Rodriguez-

Rodriguez, and Jaramillo-Jacob, 2012).

2.3.6 GC/MS Analysis

GC-MS analysis of biodiesel produced from various microalgal oil was performed on

Agilent 7000A triple quadrupole mass spectrometer, coupled to a gas chromatograph

(Agilent 7890) equipped with an auto sampler. The GC column used was a fused with

silica capillary column (Agilent 190905-433, 30m × 250 μm i.d., film thickness 0.25

μm). The pressure of the carrier gas (helium) was 7.0699 Psi at the initial oven

temperature with flow rate 64 mL min−1

. All standards and samples were injected in the

split mode (split/column flow ratio 60:1). The injector temperature was 250°C; the oven

temperature was 50 °C, rose to 220 °C at rate of 14 °C min−1

(total run time 34 min). The

mass spectrometer was operated in the electron impact (EI) mode at 70 eV in the scan

range of 50–650 m/z. The temperature of the transfer line and of the ion source was set to

a value of 320 and 280 °C, respectively. The injection sample volume was 1.0 μL. Mass

Hunter software (Agilent) was used for data acquisition and processing. Peak

identification of algal oil was performed by comparison with retention times of standards

and the mass spectra obtained compared with those available in the Wiley and NIST

libraries (Wiley Registry TM, 8th Edition Mass Spectral Library, and the NIST 08 Mass

Spectral Library (NIST/EPA/NIH) 2008 version) with an acceptance criterion of a match

above a critical factor of 80%.


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2.3.7 GC-MS/MS Quantification of FAMEs

Quantification of FAMEs (C-14:0, C-16:0 and C-18:0) was achieved in MRM mode with

the same GC conditions with collision energy of 30 eV and a solvent delay of 5 min. The

dwell time was 50 ms and the scan rate was 6.5 cycles/s. The fragment ions (listed in

Table 9) allowed quantification by using one of the three ions as the quantification ion

and the other two as qualifiers. Calibration standard solutions, with concentration

ranging between 60 to 150 ng/μL, were prepared by appropriate dilution with isooctane

in 5 mL volumetric flasks. Calibration curves were plotted by peak area analytes.

Table 9:Optimized GC-MS/MS acquisition parameters for FAMEs.

FAMEs Precursor

ion

(m/z)

Optimized

collision

energy (ev)

MRM transitions (m/z)

Identification Quantification

C-14:0 242.4 10 184.8; 157.1; 128.7 242.4>157.1

C-16:0 270.4 10 227.1; 199.0; 171.2 270.4>171.2

C-18:0 298.5 10 255.1; 212.4; 101.0 298.5>101.0


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2.4 Results and Discussion

2.4.1 Microalgae Collection and Identification

30 Marine algal samples were collected from the different locations around the coast of

Karachi, Pakistan and about 20 fresh water algal samples were collected from fresh

water ponds and lake. The average temperature of the surface water was 25–29°C for

both fresh and marine water samples. Salinity range was 0.5-1 ppt and 36–39 ppt for

fresh and marine water samples, respectively. Similarly, pH range was 6.8-7.5 and 7.7-

8.1 for fresh and marine water samples, respectively.

Table 10: Summary of samples collection.

Place of collection Date of

collection

No. of

sample

Sample codes

Fresh water environment

Karachi University May 2008 04 KU-1, KU-2, KU-3, KU-4

Malir June 2008 04 M-1, M-2, M-3, M-4

Gharo (Pond) July 2008 04 G-1, G-2, G-3, G-4

Karli Lake November 2008 06 K-1, K-2, K-3, K-4, K-5, K-6

River indus May-July 2009. 04 J-1, J-2, J-3, J-4

Total 22

Marine environment

Buleji September 2008 06 B-1, B-2, B-3, B-4, B-5, B-5, B-6

Korangi creek October 2008 04 KC-1, KC-2, KC-3, KC-4

Paradise point August 2008 06 P-1, P-2, P-3, P-4, P-5, P-5, P-6

Total 16

Grand total 38

Most of the microalgal samples were grown successfully in the laboratory and belongs to

the class Chlorophyceae, Cyanophyceae and Bacillariophyceae (Diatoms). Six

microalgal strains, KU-001, KU-002, KU-003, KU-004, KU-005, and KU-006 were

successfully isolated by using microlgal isolation techniques and identified as

Scenedesmus quadricauda, Scenedesmus acuminatus, Nannochloropsis sp., Anabaena

sp., Chlorella sp. and Oscillatoria sp. using their morphological features. Strains KU-


Page | 43

001, KU-002, KU 004, and KU 005 were obtained from fresh water while strains KU

003 and KU 006 were obtained from the marine samples. Microscopic images of purified

algal strains are presented in Figure 4.

Figure 4: Differential interference contrast (DIC) images of isolated unimicroalgal

species (A) Scenedesmusquadricauda, (B) Scenedesmus acuminatus,(C)

Nannochloropsis sp.,(D) Anabaena sp.,(E) Chlorella sp. and (F) Oscillatoria sp.


Page | 44

2.4.2 Cell Biomass Production and Comparison of Lipid Content

The purified strains were cultivated for the autotrophic biomass production. Biomass

productivity rate and percent oil content of each microalgae are summarized in Table

11.Oscillatoriasp. has the highest biomass production rate (2166.7 mg/d/L) among all

screened microalgal species. However, biomass productivity (under autotrophic

condition) of commercially available S. quadricauda (190 mg/d/L), S. acuminatus (210

mg/d/L), Nannochloropsis sp. (170–210 mg/d/L), and Chlorella sp. (230 mg/d/L) have

been investigated by Rodolfi et al. (L Rodolfi et al., 2009). Microalgae biomass was

harvested by centrifugation when growth of the microalgal species reached to its

threshold value. The lipid content in all microalgal algal species was expressed in terms

of percentage of their dry weight. S. acuminatus, S. quadricauda and Nannochloropsis

sp. showed high oil percent i.e. 17.0, 12.6 and 10.4%, respectively, while Anabaena sp.,

Chlorella sp. and sp. Showed oil content < 5.5%. Reported literature showed that S.

quadricauda, Nannochloropsis sp., Chlorella sp. and Oscillatroria sp. contain 18.4, 29.2,

18.7 and 5.0% oil, respectively, in phototrophic cultivation without any stress (Repka,

van der Vlies, and Vijverberg, 1998; Liliana Rodolfi et al., 2009). This variation in

biomass productivity and oil productivity may be due the temperature, pH, salinity and

other climatic difference. Therefore, it is important to study native microalgal species for

their biomass and oil productivity. Among all screened microalgal species, S. acuminatus

is found to be the most promising oil producing species (17.0%) and it was found to be

suitable for biodiesel production on a large scale whereas Oscillatroria sp. has shown

highest biomass productivity rate (Table 11). This specie can also be considered at

commercial scale but need to optimize its oil productivity up to the acceptable value.


Page | 45

Table 11: Biomass and algal oil productivity of various microalgal species.

S. No. Algal specie

(Codes) Habitat

Growth rate

(mg/day/L)

Oil percent

(%)

1. S. quadricauda

(KU-001) Fresh water 22.79 12.59

2. S. acuminatus

(KU-002) Fresh water 94.74 17

3. Nannochloropsis sp.

(KU-003). Marine 148 10.42

4. Anabaena sp.

(KU-004) Fresh water 275.62 2.98

5. Chlorella sp.

(KU-005) Fresh water 152.65 5.25

6. Oscillatoria sp.

(KU-006) Marine 2166.7 3.69

2.4.3 Characterization of Biodiesel

The suitable characteristics of algal species which are needed to for optimum biodiesel

production including high growth rate and high oil content. In addition to these, they

must have the right kind of FAMEs content needed for a high quality biodiesel.

Characterization of fatty acid methyl esters of synthesized biodiesel was carried out by

GC-MS. The comparative total ion chromatogram of all the samples is shown in Figure 5

Identified peaks of the fatty acid methyl esters and their relative percentages are

summarized in Table 12. Compound were identified through comparison of retention

time with standard as well as NIST database having match factor (>80%).


Page | 46

Figure 5: Total ion chromatogram (TIC) of biodiesel synthesized from microalgal oil

(A) Anabaena sp.,(B) Chlorella sp.,(C) Nannochloropsis sp.,(D) Scenedesmus

acuminatus,(E) Scenedesmusquadricauda and (F) Oscillatoria sp.


Page | 47

It was observed that, S. quadricauda, S. acuminatus, Nannochloropsis sp., Anabaena sp.,

Chlorella sp. and Oscillatroria sp. contain total saturated fatty acid methyl esters

(SAFA) of 46, 69, 47, 52, 42, and 55% respectively, while the total monounsaturated

(MUFA) were 9, 30, 22, 11, 31 and 20%, respectively. Polyunsaturated fatty acid methyl

acid (PUFA) contents were found to be 0, 1, 6, 12, 12 and 4%, respectively as shown in

figure 6. Moreover, all the species were found rich in hexadecanoic acid methyl ester(C-

16:0), ranging from 29-61%. The fatty acid profiling of microalgae ultimately affects the

quality of the biodiesel. The carbon chain length of saturated and unsaturated fatty acids

affects biodiesel properties, such as cetane number, oxidative stability and cold-flow

properties. Generally, high proportion of SAFAs and MUFAs are preferred for

increasing energy yield and superior oxidative stability. However, oils containing

MUFAs are prone to solidification at low temperatures, while oils rich in PUFAs have

very good cold-flow properties, but such biodiesel tends to be vulnerable to oxidation.

This tendency causes adverse effects on fuel conservation and combustion (Imahara,

Minami, and Saka, 2006). In strain KU-001 (S. quadricauda) no PUFA was observed.

Strains KU-002 (S. acuminatus), KU-003 (Nannochloropsis sp.) and KU-005 (Chlorella

sp.) are found to have high cetane number, as they are rich in octadecanoic acid methyl

ester (C-18:0), oleic acid methyl ester (C-18:1) and linoleic acid methyl ester (C-18:2).

Hydroxylated saturated fatty acid methyl esters were found in all microalgal samples in

high percentages, ranging between 15-45% (except S. acuminatus). Compounds of each

algal oil are mentioned in table 12.


Page | 48

Table 12:FAMEs profile of biodiesel of various microalgal species by GC-MS.

RT

(Min) Label

Microalgae (Relative percentage)

Strain

KU-001

Strain

KU-002

Strain

KU-003

Strain

KU-004

Strain

KU-005

Strain

KU-006

18.59 Dodecanoic acid methyl ester - - - - 0.68 -

20.90 Tetradecanoic acid methyl ester 1.40 1.91 0.78 7.65 1.58 0.73

22.63 7,10-Hexadecdienoic acid methyl ester - - 0.65 1.33 1.37 2.66

22.76 9-Hexadecenoic acid methyl ester - 2.92 0.65 3.12 - 1.33

22.81 11-Hexadecenoic acid methyl ester - 2.21 0.41 0.00 10.17 0.60

23.01 Hexadecanoic acid methyl ester 37.48 61.58 36.80 29.18 30.15 50.05

24.30 9(R),10(R)-Dihydroxy octadecanoic acid methyl ester 10.52 - 1.78 11.56 3.70 6.19

24.51 6,9,12-Octadecatrienoic acid methyl ester - - - - 1.12 -

24.64 9,12-Octadecadienoic acid methyl ester - - 4.19 8.92 8.69 1.28

24.69 9-Octadecenoic acid methyl ester - 21.22 11.18 5.16 7.82 11.48

24.74 11-Octadecenoic acid methyl ester 9.31 3.31 8.99 2.41 11.34 6.17

24.91 Octadecanoic acid methyl ester 6.80 3.45 5.06 6.15 7.51 4.60


Page | 49

25.06 11-Methoxy octadecanoic acid methyl ester - - - 2.63 - -

25.46 8,11-Eicosadienoic acid methyl ester - - - 0.49 - -

25.54 11,14-Eicosadienoic acid methyl ester - - 0.82 1.13 - -

25.91 8,9-Dihydroxy docosanoic acid methyl ester - - 1.18 2.14 1.38 -

26.08 10-Hydroxy octadecanoic acid methyl ester 34.49 - 22.20 9.15 9.68 14.94

26.39 9,10-Epoxy octadecanoic acid, methyl ester - 2.21 - - - -

26.44 11-Eicosenoic acid methyl ester - - 0.59 - 2.16 -

26.66 Eicosanoic acid methyl ester - - 2.09 6.98 0.44 -

26.93 13,16-Docosadienoic methyl ester - 1.20 - 0.35 0.34 -

28.27 Docosanoic acid methyl ester - - 0.84 0.95 0.73 -

29.84 Tetracosanoic acid methyl ester - - 0.72 0.70 1.14 -

31.84 Hexacosanoic acid methyl ester - - 1.08 - - -


Page | 50

Figure 6: Distribution of FAMEs among various microagal strains.

2.4.4 Properties of Biodiesel

Density, kinematic viscosity, iodine value, higher heating value and centane number for

all six microalgal biodiesels are summarized in the Table 13. Most of the properties of

the biodiesels are found within the range of ASTM or EUstandards, except for strain

KU-001(S. quadricauda) which shows low cetane number.


Page | 51

Table 13: Properties of biodiesel obtained from various microalgae.

Properties Strain

KU-001

Strain

KU-002

Strain

KU-003

Strain

KU-004

Strain

KU-005

Strain

KU-006

ASTM/EU

biodiesel

standards

Iodine value

(gm/100g of oil)

36.61 36.11 45.63 52.87 46.03 67.36 120 max

Saponification value

(mg KOH/g of oil)

237.02 220.72 228.79 232.71 230.71 209.34 -

Density

(gm/cm3)

0.476 0.838 0.622 0.647 0.642 0.874 0.82-0.90

Kinematic viscosity

(mm2/s)

2.5 4.3 3.3 3.4 3.3 4.3 1.9-6.0

Higher heating value

(MJ/Kg)

39.16 39.84 39.36 39.10 39.28 39.84 >35

Cetane number

40.0 67.5 49.6 50.3 49.8 63.6 Min. 47


Page | 52

2.4.5 Quantification of FAMEs (C-14:0, C-16:0 and C-18:0) by GC-MS/MS

Three biodiesel based fatty acid methyl esters i.e. C-14:0, C-16:0 and C-18:0 were

investigated quantitatively by gas chromatography-tandem mass spectrometry in

multiple reaction monitoring (MRM) mode (Figure 7). These three FAMEs were

selected because they were found in all the analyzed algal biodiesel and they have major

impact on the biodiesel properties.

Figure 7:(A) Full scan MS chromatogram of biodiesel synthesized from microalgal oil

Reconstructed ion chromatogram for (B) C-14:0 at m/z 242.4→72.7+100+157.1 (C) C-

16:0 at m/z 270.4→100+58.6+132 (D)C-18:0 at m/z 298.5 →72+101+100.8+198

MRM scan mode has a higher selectivity than the SIM scan mode (Lachenmeier, Frank,

and Kuballa, 2005), and thus allows a better signal resolution without a preliminary

fractionation of the oil. Only the molecules which have the selected fragmentation

patterns are taken into account. Therefore, the interferences are dramatically reduced and

the signal-to-noise ratio increases by many orders of magnitude. The first quadrupole

isolates all the M+·ions with a m/z 242.4, 270.4 and 298.5, while in the collision cell

these ions undergo a fragmentation process which yield the formation of product ions.

Collision energy (10 to 40 eV) was varied in the product ion scan and 10 eV was found

to be the most suitable for obtaining fragment ions. Characteristic fragment ions for these

three saturated fatty methyl esters are listed in Table 9. Out of three, signal best transition


Page | 53

was selected for quantification and the other two were marked as qualifiers, as indicated

in Table 9. In the third quadrupole, only the product ions were isolated and their ions

were considered in the mass chromatogram. In this way, the interferences were

eliminated from the chromatogram and only the ions with the selected precursor ion and

product ions were taken into account.

The calibration graphs ofC-14:0, C-16:0 and C-18:0were plotted using six calibration

standardsin concentration range of20-150 ng/μL.Chem station software (Agilent) was

used for plotting calibration curve taking concentration on x-axis and peak area on y-

axis. The calibration graphs of C-14:0, C-16:0 and C-18:0 are shown in Figure 8.

The linearity, LOD and LOQ of these FAMEs are also investigated in biodiesel

samplesby GC-MS/MS using calibration standards, and the results are presented in Table

14. Linear relationships between the concentration and the corresponding peak area were

found over the concentration range of 60-150 ng/μL with good correlation coefficient

(r2) ≥099.2 in all cases. The LODs and LOQ varied between from 4.27-17.65 ng/μL and

between 14.22-58.82 ng/μL, respectively.

Table 14: Retention time, correlation coefficients, LOD and LOQof FAMEs.

FAMEs Retention time

(min)

Correlation

coefficient (r2)

LOD

(ng/μL)

LOQ

(ng/μL)

C-14:0 20.91 0.998 4.27 14.22

C-16:0 23.02 0.998 17.65 58.82

C-18:0 24.91 0.992 17.20 57.33


Page | 54

Figure 8: Calibration curve of FAMEs (C-14:0, C-16:0 and C-18:0).


Page | 55

2.4.6 Application to Microalgal Biodiesel Samples

The validated GC-MS/MS method was applied to six samples of algal oil for the

determination of absolute amount of biodiesel based fatty acid methyl esters, including

C-14:0, C-16:0 and C-18:0, and the results are summarized in the Table 15. The amount

of C-14:0 is found highest in strain KU-004 and lowest in strain KU-001. Strain KU-003

shows high amounts of both C-16:0 and C-18:0, whereas strain KU-004 and Strain KU-

002 showed the lowest amounts of C-16:0 and C-18:0, respectively.

Table 15: Absolute amount of FAMEs analysis of oilgae.

Sample FAMEs (mg/g of oil)

C-14:0 C-16:0 C-18:0

Strain KU-001 3.057±0.019 45.590±1.101 24.851±0.946

Strain KU-002 3.069±0.009 67.318±0.650 8.568±0.192

Strain KU-003 3.289±0.008 83.836±1.068 36.969±0.349

Strain KU-004 6.006±0.053 30.465±0.465 21.191±0.166

Strain KU-005 3.289±0.074 39.275±1.841 27.060±1.151

Strain KU-006 3.176±0.002 66.111±1.440 23.256±0.215


Page | 56

2.5 Conclusion

Microalgae have attracted major interest as a sustainable source for biodiesel production

on commercial scale. Thisstudy involved the screening of six microalgal strains, obtained

from water bodies of southern Pakistan for the biodiesel production. Growth rate,

biomass productivity and oil content of each algal species have been investigated under

autotrophic condition. Scenedesmusacuminatus contains the highest oil content among

all six microalgal strains, whereas Oscillatroria sp. was found to have highest biomass

productivity (per day per liter). Biodiesel was produced from algal oil by acid catalyzed

transesterification reaction and resulting fatty acid methyl esters (FAMEs) content was

analyzed by GC/MS. Fatty acid profiling of the biodiesel produced from the microalgal

oil shows high content of saturated and monounsaturated FAMEs, including of C-16:0,

C-18:0, 18:1 Δcis-9

and C-18:1 Δcis-11

. Density, kinematic viscosity, iodine value, higher

heating value and cetane number of the biodiesels were found to be within the specified

range. Moreover, the three most important saturated FAMEs i.e. C-14:0, C-16:0 and C-

18:0 in microalgal biodiesels have been determined quantitatively by GC-MS/MS. C-

14:0, C-16:0 and C-18:0 by a validated GC-MS/MS method were found to be 1.5-1.7,

15.0-42.5 and 4.2-18.4 mg/g. Further efforts on screening more microalgae for biodiesel

production would be encouraging for the discovery the best microalgae that would be

feasible for biodiesel production in terms of biomass productivity and resulting oil

content in local environment.

Page | 57

3 CHAPTER 3

QUANTIFICATION OF FAMES IN

BIODIESEL BLENDS OF VARIOUS

SOURCES BY GAS

CHROMATOGRAPHY-TANDEM MASS

SPECTROMETRY

Chapter 3: Quantification of FAMEs in Biodiesel Blends.

Page | 58

3.1 Biodiesel Blends

Pure biodiesel is a mixture of fatty acid methyl esters along with some amount of trace

impurities, such as methanol, glycerol, mono-, di-, and triacylglycerol, and sterol

glucosides (Vasconcelos et. al., 2012). Whereas diesel is a complex mixture of about

75% saturated hydrocarbons (primarily paraffin including n, is, and cycloparaffins), and

25% aromatic hydrocarbons (including naphthalenes and alkylbenzenes)(Pinto et al.,

2005). The common blended forms include B-20 (20% biodiesel, 80% petroleum diesel),

B-5 (5% biodiesel, 95% petroleum diesel), and B-2 (2% biodiesel, 98% petroleum

diesel) (Abdelnur et al., 2013). The most common biodiesel blend is B-20, which

qualifies for fleet compliance under the Energy Policy Act (EPAct) of 1992. Moreover,

one biodiesel can also be blended with other biodiesel to improve the quality and

efficiency (Gerhard Knothe, 2008). Several biodiesel-biodiesel blends have been made

and various blended forms are emerging (Kuthalingam, Asokan, Marta, Skryabin, and

Karuppiah, 2013; Mejia, Salgado, and Orrego, 2013; Pardo et al., 2012; Sanjid et al.,

2014; Zuleta, Rios, and Benjumea, 2012). Blending of pure biodiesel B100 with

petroleum diesel is accomplished by one of the following methods:

Mixing in tanks at manufacturing point prior to delivery to tanker truck.

Splash mixing in the tanker truck.

In-line mixing, two components arrive at tanker truck simultaneously.

Metered pump mixing, petroleum diesel and biodiesel meters are set to X total

volume, transfer pump pulls from two points and mix is complete on leaving

pump.

Blended biodiesel are much more complex mixtures to analyze FAMEs as it depends on

nature of FAMEs (i.e. varies from source to source) as well as quality of the diesel used

for blending۔


Page | 59

3.2 Biodiesel Blends Standards

ASTM has provided specifications for biodiesel blends (Table 16). These specifications

cover biodiesel blend grades of 6 to 20% (v/v) biodiesel with diesel fuel. This can be

collectively designated as B6 to B20. These grades are suitable for various types of

diesel engines.

Table 16: ASTM D7467 specification for biodiesel-diesel blends (B6 to B20).

a The minimum viscosity shall be 1.3mm2/s.

b The minimum flash point shall be 38 °C.

c Low temperature properties are not strictly specified, but should be agreed upon by the

fuel supplier or purchaser.

Property B6–B20 Blends

Test method Limit

Viscosity (mm2/s at 40 °C ) D445 1.97-4.1

a

Density (kg/m3 min.) D1298 820

Density (kg/m3 max.) D6890 858

Flash point (°C, min.) D93 52b

Cloud point (°C, max.) D2500 C

Oxidation stability (hours, min.) EN14112 6

Acid number (mg KOH/g, max.) D664 0.3

Carbon residue (mass %, max.) D524 0.35

Ash content (mass %, max.) D2709 0.05

Sulfur (mg/kg max.) D1298 10

Biodiesel content (% v/v min.) D5453 5.1

Biodiesel content (% v/v max.) D482 20

Water and sediment (% v/v max.) EN 14078 0.05

Oxidation stability (hour, min.) D445 20

Copper corrosion (3 h at 50 °C max.) EN 14078 Class 1


Page | 60

3.3 Mass Spectrometric Quantification using SRM

/MRM Mode

Selected reaction monitoring (SRM) is a method used in tandem mass spectrometry in

which an ion of a particular mass is selected in the first stage of a tandem mass

spectrometer and an ion product of a fragmentation reaction of the precursor ion is

selected in the second mass spectrometer stage for detection(de Hoffmann, 1996).

Whereas multiple reaction monitoring (MRM) is the application of selected reaction

monitoring to multiple product ions from one or more precursor ions. Multiple reactions

monitoring (MRM) is a non‐scanning technique and generally performed on

triple‐quadrupole (QQQ) instruments in which fragmentation is used as a means to

increase selectivity. In MRM experiments, two mass analyzers are used as static mass

filters, to monitor a particular fragment ion of a selected precursor ion. The selectivity

resulting from the two filtering stages combined with the high‐duty cycle results in

quantitative analyses with unmatched sensitivity. The specific pair of m/z values

associated with the precursor and fragment ions selected is referred to as a „transition‟

(e.g., 673.5/534.3).

The term SRM or „pseudo SRM‟ is occasionally used also to describe experiments

conducted in LITs or QqTOFs instruments where, upon fragmentation of a precursor ion,

MS/MS data are acquired on a partial mass range centered on a fragment ion. Although

this scan mode resembles an SRM experiment, it is based on the „electronic‟ extraction

of the fragment ion signal (s) and can thus be essentially viewed as the SIM of fragment

ion (s). The full potential of SRM is only employed when the experiment is performed

on QQQ MS.

Multiple reactions monitoring mode has various advantages over other modes. MRM

mode shows very high selectivity and senitvity. Moreovere, its dynamic range make on

a QQQ MS ideally suited for quantification in complex samples.


Page | 61

3.4 FAMEs Analysis of Biodiesel Blends

In the literature, there are many reports about FAMEs determination in blends of

biodiesel and diesel. The list for FAMEs analysis in various biodiesel-biodiesel blends

and biodiesel-diesel blends are summarized in the table 17. These techniques include

spectroscopic and chromatographic methods. Spectroscopic methods have been used for

assessing biodiesel total FAMEs content and for monitoring transesterification reaction.

However, spectroscopic techniques alone do not provide much information about nature

of FAME in a complex mixture. Radiocarbon analysis was also reported for the

determination of percentage level of biodiesel in biodiesel-biodiesel blends (Birova,

Svajdlenka, Cvengros, and Dostalikova, 2002; Chuck, Bannister, Hawley, and Davidson,

2010; Fernandes, Gomes, da Costa, da Silva, and Veras, 2011; Oliveira, Montalvao,

Daher, Suarez, and Rubim, 2006; Scherer, Oliveira, Lima, Andrade, and Caires, 2011;

Sitko, Zawisza, Kowalewska, Kocot, and Polowniak, 2011) whereas chromatographic

techniques are very useful for determining the FAMEs in different biodiesel-biodiesel

blends (Pardo et al., 2012) while in case of biodiesel-diesel blends they have some

limitation due to the complexity of diesel composition (G. Knothe, 2001).

GC-MS/MS is the most important technique to analyze such a complex mixture. Faria et

al for the first time have quantified only one FAME i.e. methyl linoleate in soybean

biodiesel-diesel blend by GC-MS with selected ion monitoring (SIM) mode (R. C. M.

Faria, M. J. C. Rezende, C. M. Rezende, and A. C. Pinto, 2007). The developed method

can quantify very efficiently nineteen important biodiesel based fatty acid methyl esters

FAMEs in any pure biodiesel, any biodiesel-diesel and any biodiesel-biodiesel blend by

gas chromatography tandem mass spectrometry in multiple reaction monitoring (MRM)

mode.


Page | 62

Table 17: Literature survey of FAMEs analysis in biodiesel blends.

Technique Work Reference

HRGC-MS

(SIM mode)

Development and validation of a

methodology for analysis of biodiesel:

Diesel blends using gas

chromatography-mass spectrometry.

(Faria, Rezende,

Claudia M. Rezende,

and Angelo C. Pinto,

2007)

2D GC-MS Predicting percent composition of

blends of biodiesel and conventional

diesel using gas chromatography-mass

spectrometry, comprehensive two-

dimensional gas chromatography-mass

spectrometry, and partial least squares

analysis

(Pierce and Schale,

2011)

GC-FID Conventional and fast gas

chromatography analysis of biodiesel

blends using an ionic liquid stationary

phase

(Ragonese, et al.,

2009)

IR spectroscopy Determination of the mass fraction of

methyl esters in mixed fuels

(Birova et al., 2002)

FT-IR spectroscopy,

refractive index and

UV-Vis spectroscopy

Spectroscopic sensor techniques

applicable to real-time biodiesel

determination

(Chuck et al., 2010)

FT-NIR spectroscopy Determination of methyl ester contents

in biodiesel blends by FTIR-ATR and

FTNIR spectroscopies.

(Oliveira et al., 2006)

NIR and visible Determination of biodiesel content in

biodiesel/diesel blends using NIR and

(Fernandes et al.,


Page | 63

spectroscopy visible spectroscopy with variable

selection

2011)

Fluorescence

Spectroscopy

Determination of the biodiesel content

in diesel/biodiesel blends: A method

based on fluorescence spectroscopy

(Scherer et al., 2011)

X-ray spectrometry Fast and simple method for

determination of fatty acid methyl esters

(FAME) in biodiesel blends using X-ray

spectrometry

(Sitko et al., 2011)

ESI-MS Chemical fingerprinting of biodiesel

using electrospray mass spectrometry

and chemometrics: Characterization,

discrimination, identification, and

quantification in petrodiesel

(Eide and Zahlsen,

2007)

HPLC Procedure for and results of

simultaneous determination of aromatic

hydrocarbons and fatty acid methyl

esters in diesel fuels by high

performance liquid chromatography.

(Kaminski,

Gilgenast, Przyjazny,

and Romanik, 2006)

Radiocarbon analysis Determination of biodiesel blending

percentages using natural abundance

radiocarbon analysis: Testing the

accuracy of retail biodiesel blends.

(Reddy et al., 2008)


Page | 64

3.5 Experimental

3.5.1 Material and Chemicals

All FAMEs standards including methyl hexanoate (C-6:0), methyl octanoate (C-8:0),

methyl decanoate (C-10:0), methyl dodecanoate (C-12:0), methyl tetradecanoate (C-

14:0), methyl pentadecanoate(C-15:0), methyl hexadecanoate (C-16:0), methyl

octadecanoate (C-18:0), methyl eicosanoate (C-20:0), methyl docosanoate (C-22:0),

methyl tetracosanoate (C-24:0), methyl-cis-9-tetradecenoate(C-14:1 ∆cis-9

), methyl-cis-9-

hexadecenoate (C-16:1 ∆cis-9

), methyl-cis-9-octadecenoate (C-18:1 ∆cis-9

), methyl-cis-11-

octadecenoate (C-18:1 ∆cis-11

), methyl-cis,cis-9,12-octadecadienoate (C-18:2 ∆cis,cis-9,12

),

methyl-cis,cis,cis-9,12,15-octadecatrienoate (C-18:3 ∆cis-9,12,15

), methyl-cis-11-

eicosenoate (C-20:1 ∆cis-11

), methyl-cis-13-docosenoate (C-22:1 ∆cis-13

) and methyl-cis-

15-tetracosenoate (C-24:1 ∆cis-15

) were purchased from Supelco (Bellefonte, Pa, USA).

Potassium hydroxide (KOH) was purchased from Uni-Chem (England), methanol,

anhydrous magnesium sulfate (MgSO4), and hexane were all obtained from Fisher

Scientific. Sunflower seeds, soybean seeds, neem seed, rapeseed, castor seeds, cotton

seeds, linseed, corn seeds, peanuts and coconut were purchased from local market. Dry

biomass of mixed microalgal culture was obtained from Pakistan Council of Scientific

and Industrial Research (PCSIR). Diesel (N-2grade) for blends preparation was obtained

from Pakistan State Oil (PSO).

3.5.2 Preparation of Stock and Calibration Solutions

The 20 standard stock solutions-I including one internal standard (methyl

pentadecanoate) was separately prepared by dissolving 5 mg of each compound in a 5

mL volumetric flask diluted with hexane at room temperature. A mixture of all FAMEs

was prepared as a stock solution II in concentration range of 50-70 µg/mL for FAMEs

including C-10:0, C-12:0, C-14:0, C-16:0, C-18:0, C-20:0, C-22:0, C-24:0 and 350-500

µg/mL for FAMEs including C-6:0, C-8:0, C-14:1 ∆cis-9

, C-16:1 ∆cis-9

, C-18:1 ∆cis-9

, C-

18:1 ∆cis-11

, C-18:2 ∆cis, cis-9,12

, C-18:3 ∆cis-9,12,15

, C-20:1 ∆cis-11

,C-22:1 ∆cis-13

and C-24:1

∆cis-15

using hexane. Five different levels of calibration standard solutions were prepared

in the concentration range of 50 to 500 µg/mL using hexane from stock standard

solution-II. Pentadecanoic acid methyl ester (75 µg) was added as internal standards in 1


Page | 65

mL of each standard solution. Calibration curves were plotted against the peak area ratio

of analytes to internal standard concentration.

3.5.3 Biodiesel Preparation

Biomass (1kg) of various seeds/fruits of plant including corn, sunflower, soybean,

rapeseed, peanut, coconut, castor, algae, cotton, linseed, neem and microalgae were

soaked for three days in petroleum ether (2L each). Extracts were filtered and the solvent

was removed with rotatory evaporator to obtain oil. The biodiesel was produced by basic

methanolysis of all oils using a methanol/oil molar ratio of 12:1, with 1% potassium

hydroxide by weight as the catalyst. The reaction temperature and time were 60 °C and 1

h, respectively(Keera, El Sabagh, and Taman, 2011). After completion, the reaction

mixture was transferred to separating funnel, washed thoroughly with water and

biodiesel was extracted with hexane (3x IL) and dried with anhydrous magnesium

sulfate. Hexane was evaporated from the biodiesel by using rotary evaporator (N-1000,

Eyela, Japan).

3.5.4 Blend Preparation

The commercial grade (No. 2) diesel fuel was used for biodiesel-diesel blend

preparation. Several biodiesel-diesel blends with a wide composition range were

prepared by mixing with diesel. B-2 (2% biodiesel and 98% diesel by volume), B-5 (5%

biodiesel and 95% diesel by volume) and B-20 (20% biodiesel and 80% diesel by

volume) blends were prepared due to their widespread use. Neat fuels are designated as

B-100. One biodiesel-biodiesel blend (BB-100) was also prepared by mixing all

biodiesel produced in equal volume and its blends with diesel (BB-20, BB-5 and BB-2)

were also produced. Blends were prepared on a volume basis at room temperature.

3.5.5 Sample Preparation

Samples solutions were prepared in 5 mL volumetric flasks. 100 µL for pure biodiesel

and 1 mL for biodiesel-diesel blends were quantitatively transferred into 5 mL

volumetric flasks and made up to volume with hexane. Sample solution was filtered with

a 0.45-mm PTFE fluoropore syringe driven filter unit (Millipore, Bedford, USA) in GC

vials and subjected to analysis.


Page | 66

3.5.6 GC-MS/MS Analysis

GC-MS/MS analyses of biodiesel produced from various oils were performed on Agilent

7000A Triple quadrupole mass spectrometer coupled to a gas chromatograph (Agilent

7890) equipped with an auto sampler. The GC column used was a fused DB wax column

Agilent (30m × 250 μm i.d., film thickness 0.25 μm). The pressure of the carrier gas

(helium) was 9.83 psi at the initial oven temperature, and its flow rate 1.2 mL min−1

. All

standards and samples were injected in split mode (split/column flow ratio 100:1). The

injector temperature was 250 °C; the oven temperature was 50 °C, rose to 220 °C at rate

of 14 °C min−1

(total run time 30 min). The mass spectrometer was operated in the

electron impact (EI) mode at 70 eV in the scan range of 50-650 m/z. The temperature of

the transfer line and of the ion source was set to a value of 320 °C and 280 °C,

respectively. The injection sample volume was 1.0 µL. Mass Hunter software (Agilent)

was used for data acquisition and processing. MRM mode was used for quantification of

FAMEs with collision energy of (5-45) eV and a solvent delay of 5 min. The dwell time

was 50 ms and the scan rate was 6.5 cycles/s. The fragment ions (listed in Table 18)

allow quantification by using one of the four ions as the quantification ion and the other

ions as qualifiers.

3.5.7 Method Validation

Developed method was validated against various parameter included precision, accuracy,

regression coefficient, limit of detection and limit of quantification. Precision and

accuracy were determined with standard solutions. Precisions expressed as percentage of

relative deviation (RSD) of the method from the calibration .samples. Limit of detection

(LOD) and limit of quantification (LOQ) were determined using calibration curve. The

LOD was calculated three times of the residual standard deviation of a regression line

divided by slope of the calibration curve and LOQ was calculated ten times of the

residual standard deviation of a regression line divided by slope of the calibration curve.


Page | 67


3.6.1 Method Optimization

GC-MS/MS multiple reactions monitoring (MRM) method for quantification of FAMEs

was optimized in the following steps: (1) determining the retention time of each FAME

(2), selecting primary precursor ions, (3) optimizing the collision energy, and (4)

establishing multiple reaction monitoring (MRM) transitions.

For determination of retention time of each FAME with effective separation, more than

45 GC-EI-TIC full scans were recorded. Initially starting oven temperature was set at 50

°C which changes at the rate of 2 °C/min and reaches to the temperature of 220 °C with

total run time 35 min but this temperature program showed several overlapped peaks of

FAMEs. Therfore, several modifications were made in the GC oven temperature

program, flow rate of carrier gas and injection volume of sample. Optimized temperature

program starts oven temperature at 50 °C which changes at the rate of 14 °C/min and

reaches to the temperature of 220 °C in 30 min. Flow rate of the carrier gas was

optimized at 1.2 mL per min. Analysis was performed in split mode (split/column flow

ratio 100:1) with injection volume of 1µL. The obtained optimized parameters provide

the separations of twenty FAMEs within 30 minutes analysis time. Well resolved peaks

of all twenty FAMEs including eleven saturated FAMEs i.e. C-6:0, C-8:0, C-10:0, C-

12:0, C-14:0, C-15:0, C-16:0, C-18:0, C-20:0, C-22:0 and C-24:0, seven

monounsaturated FAMEs i.e. 14:1 ∆cis-9

, C-16:1 ∆cis-9

, C-18:1 ∆cis-9

, C-18:1 ∆cis-11

, C-

20:1 ∆cis-11

, C-22:1 ∆cis-13

and C-24:1 ∆cis-15

and two polyunsaturated FAMEs i.e. C-18:2

∆cis,cis-9,12

and C-18:3 ∆cis-9,12,15

were observed in optimized conditions on DB-Wax

column (Figure 9). Regioisomers i.e. methyl-cis-9-octadecenoate and methyl-cis-11-

octadecenoate are also well separated.


Page | 68

Figure 9: MRM chromatogram of FAMEs standards.


Page | 69

The choice of precursor ion is based on selectivity rather than signal intensity. As most

of FAMEs differ fourteen mass unit and produce almost similar fragments therefore

molecular ions were used as precursor ions. It was observed that saturated FAMEs

showed intense molecular peak or precursor peak as compared with unsaturated FAMEs.

These precursor ions were scanned for the product ions formed under different collision

energies varying from 5 to 40 eV and the most suitable collision energies for obtaining

fragment ions with significant abundance was in the range of 5-15 eV. Characteristic

fragment ions and optimized collision energy for all fatty acid methyl esters are listed in

Table 18.

These product ions spectra were analyzed critically and three product ions with high

responses were chosen, among them, the product ion with higher abundance was used for

quantification, and the product ions with lower abundance were used for qualification.

The ratio between these two ions was used to confirm the compound. Chromatograms

and spectra (quantifier and qualifiers) of all FAMEs standards are shown in Figure 10-

13.

The optimized gas chromatography mass spectrometric method was run in multiple

reactions monitoring (MRM) mode which consist of thirty-four time segments. This

method not only allows a better signal resolution but it also eliminates the preliminary

fractionation of the pure biodiesel or their biodiesel-diesel blends because in MRM

mode.Only those FAMEs molecules which have the selected fragmentation patterns are

taken into account in contrast with TIC scan mode. In this way the interferences

considerably reduced and the signal to noise ratio increases in many orders of magnitude.

For instance, analysis of peanut oil biodiesel (B-100) and their blends (B-2, B-5 and B-

20) in TIC scan showed signal to noise ratio in the range of 10-160 while the same

sample under developed MRM method showed signal to noise ratio in the range of 120-

1556 (Figure 14).


Page | 70

Table 18: Optimized GC–MS/MS acquisition method parameters and list of precursor

ions and product ions of each FAMEs.

FAMEs

Precursor

ion

(m/z)

Optimized

collision

energy (eV)

MRM transitions (m/z)

Identification Quantification

C-6:0 130.1 15 101, 77, 74 130.0 > 43.0

C-8:0 158.2 15 115, 87, 59 158.0 > 73.0

C-10:0 186.2 05 157, 129, 115 186.0 > 143.0

12:0 214.3 05 185, 171, 156 214.0 > 143.0

C-14:0 242.4 05 157, 129, 114 242.0 > 198.9

C-14:1 ∆cis-9

240.3 05 208, 183, 140 240.0 > 155.0

C-15:0 256.0 05 213, 185, 143 256.0 > 157.0

C-16:0 270.4 05 227, 185, 171 270.0 > 199.0

C-16:1 ∆cis-9

268.4 05 236, 154, 127 268.0 > 141.0

C-18:0 298.5 05 255, 157, 101 298.0 > 185.0

C-18:1 ∆cis-9

296.4 05 253, 213, 155 296.0 > 141.0

C-18:1 ∆cis-11

296.4 05 253, 213, 155 296.0 > 141.0

C-18:2 ∆cis, cis-9,12

294.4 05 262, 150, 82 294.0 > 96.0

C-18:3 ∆cis-9,12,15

292.4 05 135, 121, 80 292.0 > 94.0

C-20:0 326.5 05 241, 213, 171 326.0 > 199.0

C-20:1 ∆cis-11

324.5 05 239, 155, 101 324.0 > 141.0

C-22:0 354.6 05 297, 269, 227 354.0 > 213.0

C-22:1 ∆cis-13

352.5 05 320, 269, 127 352.0 > 141.0

C-24:0 382.6 05 241, 227, 185 382.0 > 199.0

C-24:1 ∆cis-15

380.6 05 227, 171, 115 380.0 > 143.0


Page | 71

Figure 10: Extracted ion chromatograms and product ions spectra (Quantifier and

qualifiers) of FAMEs (C-6:0, C-8:0, C-10:0, C-12:0 and C-14:0).


Page | 72



, C-16:0, C-16:1 ∆cis-9

, C-18:0 and C-18:1 ∆cis-9

).


Page | 73



, C-18:2 ∆cis, cis-9,12

, C-18:3 ∆cis-9,12,15

, C-20:0 and C-

20:1 ∆cis-11

).


Page | 74


qualifiers) of FAMEs (C-22:0, C-22:1 ∆cis-13

, C-24:0 and C-24:1 ∆cis-15

).


Page | 75

Figure14:(A) and (B): TIC scan and MRM of diesel, respectively; (C) and (D): TIC scan

and MRM of B- 2 biodiesel-diesel blend, respectively; (E) and (F): TIC scan and MRM

of B- 5 biodiesel-diesel blend, respectively; (G) and (H): TIC scan and MRM of B- 20

biodiesel-diesel blend, respectively; (I) and (J): EI TIC scan and MRM of Peanut

biodiesel (B-100), respectively.


Page | 76

3.6.2 Calibration Curves of Standard

Linearity of an analytical method is its capacity to attain test responses that are directly

proportional to the amount of analyte present in the sample. In order to demonstrate this,

standard solution of the test substances are diluted or separately weighed. Linearity is

determined by 3 replicates injections of multiple standards with the concentrations

ranging between 80–120 percent of the anticipated range of concentration. The amount

of the standards must be directly proportional to the detector response or proportional via

well-defined mathematical model. Concentration ranges of each FAME for calibration

standards are given in table 19.

Table 19: Concentration ranges of each FAME for calibration standards.

FAMEs Calibration standard (µg/mL)

1 2 3 4 5

C-6:0 364.25 318.60 272.93 227.31 181.78

C-8:0 386.15 337.75 289.33 240.98 192.70

C-10:0 60.18 52.64 45.09 37.56 30.03

C-12:0 69.03 60.38 51.72 43.08 34.45

C-14:0 68.37 59.80 51.23 42.66 34.12

C-14:1 ∆cis-9

54.58 47.74 40.89 34.06 27.24

C-16:0 51.30 44.87 38.44 32.02 25.60

C-16:1 ∆cis-9

55.71 48.73 41.74 34.77 27.80

C-18:0 47.28 41.36 35.43 29.51 23.60

C-18:1 ∆cis-9

41.22 36.05 30.89 25.72 20.57

C-18:1 ∆cis-11

432.30 378.11 323.91 269.77 215.73

C-18:2 ∆cis, cis-9,12

313.74 274.41 235.08 195.79 156.57

C-18:3 ∆cis-9,12,15

410.96 359.44 307.92 256.46 205.08

C-20:0 464.00 405.84 347.66 289.56 231.55

C-20:1 ∆cis-11

488.36 427.14 365.92 304.76 243.71

C-22:0 487.39 426.29 365.19 304.15 243.23

C-22:1 ∆cis-13

397.57 347.73 297.89 248.10 198.40

C-24:0 335.15 293.14 251.12 209.15 167.25

C-24:1 ∆cis-15

424.77 371.53 318.27 265.08 211.98

The calibration graphs were plotted using chem station software (Agilent) taking

concentration on x-axis and peak area on y-axis. The calibration graphs are shown in

Figure 15-21.


Page | 77

Figure 15: Calibration curve of FAMEs (C-6:0, C-8:0, C-10:0, C-12:0 and C-14:0).


Page | 78

Figure 16: Calibration curve of FAMEs (C-12:0, C-14:0 and C-14:1 ∆ cis-9

).


Page | 79

Figure 17: Calibration curve of FAMEs (C-16:0, C-16:1 ∆ cis-9

and C-18:0).


Page | 80

Figure 18: Calibration curve of FAMEs (C-18:1 ∆cis-9

, C-18:1 ∆cis-11

, C-18:2 ∆cis, cis-9,12

).


Page | 81

Figure 19: Calibration curve of FAMEs (C-18:3 ∆cis-9,12,15

, C-20:0 and C-20:1 ∆cis-11

).


Page | 82


and C-24:0).


Page | 83

Figure 21: Calibration curve of FAME C-24:1 ∆cis-15

.


Method validation is the process that is used for the confirmation of method‟s suitability.

Method validation is needed for testing the quality, reliability and consistency of

analytical results. Validation of the method was performed by evaluating the various

parameters including precision, accuracy, regression coefficient, limit of detection and

limit quantification.

3.6.3.1 Linearity

Linear relationships between the concentration and the corresponding peak area were

found over the concentration range of 20-70 μg/mL for C-10:0, C-12:0, C-14:0, C-16:0,

C-18:0, C-20:0, C-22:0 and24:0 and 300-500 μg/mL for C-6:0, C-8:0, C-14:1 ∆cis-9

, C-

16:1 ∆cis-9

, C-18:1 ∆cis-9

, C-18:1 ∆cis-11

, C-18:2 ∆cis,cis-9,12

, C-18:3 ∆cis-9,12,15

, C-20:1 ∆cis-11

,

C-22:1 ∆cis-13

and C-24:1 ∆cis-15

FAMEs with correlation coefficient (r2) ≥0.96. The

correlation coefficient and regression equations of FAMEs are listed in Table 20.

3.6.3.2 Limit of Detection (LOD) and Limit of Quantification (LOQ)

Limit of detection (LOD) is also called the detection limit or lower limit of detection. It

is the lowest quantity of a substance that can be distinguished from the absence of that

substance (a blank value) within a stated confidence limit (generally 1%). The limit of

detection of each FAME is given in table 20. Result showed that LOD for saturated and

unsaturated FAMEs were varied from 0.156-109.915 ng/μL. The quantification limit of


Page | 84

an individual analytical procedure is the lowest amount of analyte in a sample which can

be quantitatively determined with suitable precision and accuracy. The limit of

quantification of each FAME is given in table 20. Results showed that LOQ for saturated

and unsaturated FAMEs were varied from and between 0.473-333.077 ng/μL.

3.6.3.3 Accuracy

The accuracy of a measurement is how close a result comes to the true value. The

accuracy of a measurement is determined by calibration of the analytical method with a

known standard. The accuracy of all nineteen FAMEs analysis is summarized in table 21

Results showed that accuracy of all FAMEs was in the range of 103-119% (Table 21).

3.6.3.4 Precision

Precision is the reproducibility of multiple measurements. It is usually described by the

standard deviation, standard error, or confidence interval. The precision of the method

was expressed in terms of percentage relative standard deviation (% R.S.D). The

precision of all nineteen FAMEs analysis is summarized in table 21. Results showed that

there is no significant variation (<2%) were observed in the analysis of all FAMEs

(Table 21).

3.6.3.5 Selectivity

Selectivity refers to the extent to which a method can determine particular analytes in

mixtures or matrices without interferences from other components. The selectivity of the

GC-MS/MS procedure was based on monitoring the appropriate MS-MS transitions for

each FAME by selecting the adequate precursor and product ions. The extracted

chromatograms of FAMEs showed that there is no other peak at the retention time of a

particular FAME (Figure 10-13).


Page | 85

Table 20: Retention time, correlation coefficient, regression equation, LOD and LOQ of individual FAMEs.

FAMEs Retention time

(min)

Correlation coefficient

(r2)

Regression equation LOD

(ng/μL)

LOQ

(ng/μL)

C-6:0 5.91 0.9667 y = 0.001075* x -0.02687 2.403 7.281

C-8:0 8.84 0.9814 y = 0.003893* x -0.008230 27.472 83.250

C-10:0 11.51 0.9908 y = 0.011202* x -0.010399 5.177 15.689

C-12:0 13.90 0.9862 y = 0.014175* x -0.028967 12.221 37.033

C-14:0 16.07 0.9857 y = 0.016921* x -0.029577 16.507 50.020

C-14:1 ∆cis-9

16.46 0.9729 y = 0.002039* x -0.013561 11.236 34.048

C-16:0 18.06 0.9874 y = 0.025375* x -0.031673 24.494 74.223

C-16:1 ∆cis-9

18.32 0.9714 y = 0.006187*x -0.005610 64.817 196.415

C-18:0 19.88 0.9697 y = 0.020282*x -0.029012 15.340 46.485

C-18:1 ∆cis-9

20.07 0.9682 y = 0.005780*x +0.013865 93.470 283.242

C-18:1 ∆cis-11

20.13 0.9669 y = 0.005606*x -0.013159 94.354 285.922

C-18:2 ∆cis, cis-9,12

20.48 0.9691 y = 0.006045) *x -0.023347 109.915 333.077

C-18:3 ∆cis-9,12,15

21.04 0.9686 y = 0.002145*x +0.005868 20.034 60.708

C-20:0 21.57 0.9782 y = 0.018401* x -0.008547 21.274 64.465

C-20:1 ∆cis-11

21.76 0.9790 y = 0.003277* x -.026381 21.517 65.204

C-22:0 23.28 0.9765 y = 0.012060* x -0.014111 5.636 17.079

C-22:1 ∆cis-13

23.53 0.9646 y = 0.002272* x -0.001360 6.604 20.012

C-24:0 25.63 0.9786 y = 0.0111203* x -0.003674 3.032 9.187

C-24:1 ∆cis-15

26.01 0.9705 y = 0.0002079 *x -0.001206 0.156 0.473


Page | 86

Table 21:Precision and accuracy for all FAMEs standards in QC samples.

FAMEs Conc.

(ng/μl)

Found

(ng/μl)

R.S.D.

(%)

Accuracy

(%)

C-6:0 318.60 329.48±2.786 0.85 103.42

C-8:0 337.75 383.47±1.64 0.43 113.54

C-10:0 52.64 59.63±0.72 1.21 113.28

C-12:0 60.38 69.02±0.69 1.00 114.31

C-14:0 59.80 68.50±0.21 0.30 114.56

C-14:1 ∆cis-9

378.11 437.68±436.36 5.50 115.76

C-16:0 47.74 55.20±0.45 0.82 115.62

C-16:1 ∆cis-9

274.41 319.59±4.93 1.54 116.46

C-18:0 44.87 52.16±1.17 2.24 116.25

C-18:1 ∆cis-9

359.44 374.40±7.72 2.06 104.16

C-18:1 ∆cis-11

405.84 467.65±11.20 2.39 115.23

C-18:2 ∆cis, cis-9,12

427.14 489.72±14.43 2.95 114.65

C-18:3 ∆cis-9,12,15

426.29 489.50±16.56 3.38 114.83

C-20:0 48.73 55.69±2.52 4.53 114.30

C-20:1 ∆cis-11

347.73 396.82±14.29 3.60 114.12

C-22:0 41.36 48.17±1.99 4.14 116.49

C-22:1 ∆cis-13

293.14 306.14±3.37 1.10 104.44

C-24:0 36.05 41.78±1.72 4.11 115.89

C-24:1 ∆cis-15

371.53 442.34±10.75 2.43 119.06


Page | 87

3.6.4 Application to Biodiesel Samples

The developed GC-MS/MS method was applied on various biodiesels and their blends

obtained from different vegetative and non-vegetative sources including peanut oil,

cotton seed, soybean oil, rapeseed oil, sunflower seed oil, coconut oil, castor seed oil,

waste cooking oil, neem seed oil, linseed oil and microalgal oil. Results showed that

relative standard deviation of samples anaylsis never exceed from 13.5%. C-16:0, C-

18:0, C-18:1 ∆cis-9

, C-18:2 ∆cis, cis-9,12

and C-18:3 ∆cis-9,12,15

are found in most of the

biodiesel and their blends as major component while others were present as minor

FAMEs. Minor FAMEs were quantified successfully in pure biodiesel and their blends

up to the 5%, below this, FAMEs concentration become too low and cross the limit of

detection. It was also observed that signal-to-noise ratio decreases slightly as percentage

of diesel in the blends of biodiesel increases. This is because of the decrease in

concentration of the target compounds. FAMEs were also quantified successfully in the

biodiesel-biodiesel blend (BB-100) and its blends with diesel (BB-20, BB-5 and BB-2)

in various percentages which is very difficult to quantify in normal GC-MS in TIC scan

mode, as they have different components and peaks overlapping observed in most of the

cases but the developed method is free from these problems.

3.6.4.1 Analysis of Peanut Biodiesel

Peanut (Arachis hypogaea) is a powerful source of biofuel as they contain about 50% oil

by weight(Nguyen, Do, and Sabatini, 2010). Gas chromatographic tandem mass

spectrometric (GC-MS/MS) analysis of peanut biodiesel using developed method

showed that peanut biodiesel contains C-16:0, C-18:0, C-18:1 ∆cis-9

, C-18:1 ∆cis-11

, C-

18:2 ∆cis,9,12

, C-20:0, C-20:1 ∆cis-11

, C-22:0 and C-24:0fatty acid methyl esters (Figure

22). C-16:0, C-18:1 ∆cis-9

, C-18:1 ∆cis-11

and C-18:2 ∆cis,9,12

were found as a major

component of the biodiesel whereas C-20:0, C-20:1 ∆cis-11

, C-22:0 and C-24:0 were

found as minor components. Results are shown in Table 22

3.6.4.2 Analysis of Rapeseed Biodiesel

Rapeseed (Brassica napus) is also known as oilseed rape and one particular group of

cultivars also called it “Canola”. It is the most common source of biodiesel used in

Europe. GC-MS/MS analysis of rapeseed biodiesel using developed method showed that

rapeseed biodiesel contains C-14:0, C-16:0, C-18:0, C-18:1 ∆cis-9

, C-18:1 ∆cis-11

, C-18:2


Page | 88

∆cis,9,12

, C-18:3 ∆cis-9,12,15

and C-20:1 ∆cis-11

fatty acid methyl esters(Figure 23).C-16:0, C-

18:1 ∆cis-9

, C-18:1 ∆cis-11

, C-20:1 ∆cis-11

were found as a major component of the biodiesel

whereas C-14:0, C-18:0, C-18:2 ∆cis,9,12

and C-18:3 ∆cis-9,12,15

were found as minor

component. Some minor components were not detected in the B-2 and B-5 biodiesel

blends as they are found below detection limit. Results are shown in Table 22.

3.6.4.3 Analysis of Cottonseed Biodiesel

Cottonseed (Gossypium sp.) is non-edible oil source; therefore there is no food versus

fuel conflict(Nabi, Rahman, and Akhter, 2009). GC-MS/MS analysis of cottonseed

biodiesel using developed method showed that cotton seed biodiesel contains FAMEs C-

16:0, C-18:0, C-18:1 ∆cis-9

and C-18:2 ∆cis,9,12

fatty acid methyl ester (Figure 24). C-16:0,

C-18:1 ∆cis-9

and C-18:2 ∆cis,9,12

were found as a major components of the biodiesel

whereas C-18:0 was found as minor component. FAMEs C-18:0 and C-18:2 ∆cis,9,12

were

not detected in the B-2 biodiesel blends as they were found below detection limit.

Results are summarized in Table 22.

3.6.4.4 Analysis of Soybean biodiesel

Soybean (Glycine max) is a major crop throughout much of North America, South

America and Asia. It is a major feedstock for biodiesel production. GC-MS/MS analysis

of soybean biodiesel using developed method showed that soybean biodiesel contains C-

16:0, C-18:0, C-18:1 ∆cis-9

, C-18:1 ∆cis-11

, C-18:2 ∆cis,9,12

, C-18:3 ∆cis-9,12,15

, C-20:0, C-

20:1 ∆cis-11

and C-22:0fatty acid methyl esters (Figure 25). C-16:0, C-18:0, C-18:1 ∆cis-9

,

C-18:1 ∆cis-11

, C-18:2 ∆cis,9,12

and C-18:3 ∆cis-9,12,15

were found as a major components

whereas C-20:0 C-20:1 ∆cis-11

and C-22:0 were found as minor components. Results are

shown in Table 22.

3.6.4.5 Analyis of Sunflower biodiesel

GC-MS/MS analysis of sunflower(Helianthus annuus) biodiesel using the developed

method showed that thebiodiesel contains C-16:0, C-18:0, C-18:1, ∆cis-9

, C-18:1, ∆cis-11

,

C-20:0, C-20:1 ∆cis-11

, 22:0, 22:1 ∆cis-13

fatty acid methyl esters (Figure 26). C-18:1, ∆cis-

9,C-20:1 ∆

cis-11, 22:1 ∆

cis-13were found as major component of the biodiesel whereas C-

16:0, C-18:0, C-18:1, ∆cis-11

, C-20:0, 22:1 ∆cis-13

were found as minor components. some


Page | 89

FAMEs were not detected in the B-2 or B-5 biodiesel blends as they were found below

detection limit. Results were shown in Table 22.

3.6.4.6 Analysis of Coconut Biodiesel

Coconut (Cocos nucifera) biodiesel can be used in Philippines, Vanuatu, Samoa, and

several other tropical island countries as an alternative fuel source to run automobiles,

trucks, and buses, and to power generators (Nakpong and Wootthikanokkhan, 2010).

GC-MS/MS analysis of coconut biodiesel using developed method showed that coconut

biodiesel contains C-6:0, C-8:0, C-10:0, C-12:0, C-14:0, C-16:0, C-18:0, C-18:1 ∆cis-9

,

C-18:1 ∆cis-11

and C-18:2 ∆cis,9,12

fatty acid methyl esters. The chromatogram shows most

intense peak of C-12:0 (Figure 27). Other major FAMEs were C-6:0, C-8:0, C-10:0, C-

14:0, C-16:0 and C-18:1 ∆cis-11

whereas C-18:0, C-18:1 ∆cis-9

and C-18:2 ∆cis,9,12

were

found as minor component of the biodiesel. Results are shown in Table 21.

3.6.4.7 Analysis of Castor Seed Biodiesel

The castor (Ricinus communis) biodiesel has very interesting properties i.e. it has very

low cloud and pour points, therefore this fuel is very suitable for using in extreme winter

temperatures (Forero, 2005). GC-MS/MS analysis of castor seed biodiesel using

developed method showed that castor seed biodiesel contains FAMEs C-16:0, C-18:0, C-

18:1 ∆cis-9,

C-18:2 ∆cis,9,12

fatty acid methyl esters (Figure 28). C-18:0, C-18:1 ∆cis-9

and C-

18:2 ∆cis,9,12

were found as a major component of the biodiesel whereasC-16:0 was found

as a minor components. C-18:1 ∆cis-9

and C-18:2 ∆cis,9,12

were not detected in the B-5 and

B-2 biodiesel blends, similarly all FAMEs were not detected in B-2 biodiesel blends as

they were found below detection limit. Results are shown in Table 22.

3.6.4.8 Analysis of Neem Biodiesel

Neem (Azadirachta indica) seed is one of the non-edible sources of biodiesel (Sardar et

al., 2011). GC-MS/MS analysis of neem biodiesel using developed method showed that

neem biodiesel contains FAMEs C-14:0, C-16:0, C-18:0, C-18:1 ∆cis-9,

C-18:1 ∆cis-11

and

C-20:0fatty acid methyl esters (Figure 29). C-14:0, C-16:0, C-18:0 and C-18:1 ∆cis-9,

were found as a major component of the biodiesel whereas C-18:1 ∆cis-11

and C-20:0

were found as minor components. C-18:1 was not detected in the B-2 and B-5 biodiesel


Page | 90

blends, similarly C-20:0was not detected in the B-2, B-5 and B-20 biodiesel blends as

they were found below detection limit. Results are shown in Table 22.

3.6.4.9 Analysis of Linseed Biodiesel

Linseed plant (Linum usitatissimum) contains high amount of oil in its seeds (around

40%) which can be converted to bio-diesel (Dixit, kanakrajand Rehman, 2012). EI TIC

MRM of linseed biodiesel-diesel blends (B-2, B-5 and B-20) and pure linseed biodiesel

(B-100) is given in Figure 30. GC-MS/MS analysis of castor seed biodiesel using

developed method showed that castor seed biodiesel contains C-14:0, C-16:0, C-18:0, C-

18:1 ∆cis-9,

C-18:1 ∆cis-11,

C-18:2 ∆cis,9,12,

C-18:3 ∆cis-9,12,15

C-20:0 fatty acid methyl esters

while C-16:0, C-18:0, C-18:1 ∆cis-9,

C-18:1 ∆cis-11,

C-18:2 ∆cis,9,12,

C-18:3 ∆cis-9,12,15

were

found as a major component of the biodiesel whereas C-14:0 and C-20:0 were found as

minor components. Concentration of each FAME of linseed biodiesel is given Table 22.

3.6.4.10 Analysis of Microlagal Biodiesel

GC-MS/MS analysis of microalgal biodiesel using developed method showed biodiesel

contains FAMEs C-12:0, C-14:0, C-16:0, C-18:0, C-18:1 ∆cis-9,

C-18:2 ∆cis,9,12,

C-18:3

∆cis-9,12,15,

C-22:1 ∆cis-13,

C-24:0fatty acid methyl esters(Figure 31). C-12:0, C-14:0, C-

16:0, C-18:1 ∆cis-9,

C-18:2 ∆cis,9,12

and C-18:3 ∆cis-9,12,15

FAMEs were found as a major

component of the biodiesel whereas C-18:0, 22:1 ∆cis-13

and C-24:0 were found as minor

components. Minor components C-18:1 ∆cis-9

and C-24:0 were not detected in the B-2

biodiesel blends as they were found below detection limit. Concentrations of FAMEs are

shown in Table 22.

3.6.4.11 Analysis of Waste Cooking oil Biodiesel

The origin of used cooking oil determines its fatty acid composition or duration of

exposure to heat and oxygen. Oil degradation during cooking occurs through three main

reactions: thermolytic, oxidative, and hydrolytic reactions. In thermolytic reactions, the

reaction occurs in absence of oxygen. High temperature is required to decompose

saturated fatty acids to form alkanes, fatty acids, ketones, esters, diacylglycerides, etc. In

addition, dimeric compounds appear to be the main derivatives as a result of thermolytic

reactions of unsaturated fatty acids. In the presence of oxygen, oxidative and

nonoxidative reactions will occur simultaneously. Oxidative reactions occur in a series of


Page | 91

initiation, propagation, and termination steps. Hydrolytic reactions take place between oil

and water formed during food preparation. Formations of DAG, MAG, FFA, and

glycerol are main derivatives from hydrolysis of TAG(Nawar, 1984). The waste cooking

oil used for biodiesel production was sunflower oil which was used for frying purpose

several times. The result showed biodiesel composed of mainly C-18:0, C-18:1 ∆cis-9

, C-

18:1 ∆cis-11

, C-22:1 ∆cis-13

. The oil was mostly degraded therefore FAMEs concentration

was too low (Figure 32) and most of the FAMEs concentration was below detection limit

especially in B-2 and B-5 (Table 22).

3.6.4.12 Analysis of Biodiesel-Biodiesel Blends

GC-MS/MS analysis of biodiesel-biodiesel blends using developed method showed that

biodiesel-biodiesel blends contains C-6:0, C-8:0, C-10:0, C-12:0, C-14:0, C-14:1 ∆cis-9

,

C-16:0, C-16:1 ∆cis-9,

C-18:0, C-18:1 ∆cis-9

, C-18:1 ∆cis-11

,C-18:2 ∆cis,9,12

, C-18:3 ∆cis-

9,12,15, C-20:0, C-20:1 ∆

cis-11, C-22:0, C-22:1 ∆

cis-13and C-24:1 ∆

cis-15fatty acid methyl

esters (Figure 33). C-8:0, C-12:0, C-14:0, C-16:0,C-18:0, C-18:1 ∆cis-9

, C-18:1 ∆cis-11

,C-

18:2 ∆cis,9,12

, C-18:3 ∆cis-9,12,15

were found as a major component whereas C-6:0 C-10:0 C-

14:1 ∆cis-9

C-16:1 ∆cis-9

C-20:0 C-20:1 ∆cis-11

, C-22:0 C-22:1 ∆cis-13

C-24:1 ∆cis-15

were

found as minor components of the biodiesel. Some minor FAMEs were not detected in

the B-2 and B-5 biodiesel blends as they were found below detection limit. Results are

shown in Table 22.


Page | 92

Figure 22: MRM chromatogram of Peanut(A) B-2,(B) B-5, (C) B-20and(D) B-100.


Page | 93

Figure 23: MRM chromatogram of Rapeseed (A) B-2, (B) B-5, (C) B-20 and (D) B-100.


Page | 94

Figure 24: MRM chromatogram of Cotton seed (A) B-2, (B) B-5, (C) B-20 and (D) B-100.


Page | 95

Figure 25: MRM chromatogram of Soybean (A) B-2, (B) B-5, (C) B-20 and (D) B-100.


Page | 96

Figure 26: MRM chromatogram of Sunflower (A) B-2, (B) B-5, (C) B-20 and (D) B-100.


Page | 97

Figure 27: MRM chromatogram of Coconut (A) B-2, (B) B-5, (C) B-20 and (D) B-100.


Page | 98

Figure 28: MRM chromatogram of Castor seed (A) B-2, (B) B-5, (C) B-20 and (D) B-100.


Page | 99

Figure 29: MRM chromatogram of Neem seed (A) B-2, (B) B-5, (C) B-20 and (D) B-100.


Page | 100

Figure 30: MRM chromatogram of Linseed (A) B-2, (B) B-5, (C) B-20 and (D) B-100.


Page | 101

Figure 31: MRM chromatogram of microalgae (A) B-2, (B) B-5, (C) B-20 and (D) B-100.


Page | 102

Figure 32: MRM chromatogram of waste cooking oil (A) B-2, (B) B-5, (C) B-20 and (D) B-100.


Page | 103

Figure 33: MRM chromatogram of biodiesel-biodiesel-diesel blends (A) BB-2, (B) BB-5, (C) BB-20 and biodiesel-diesel blends (D)BB-100.


Page | 104

Table 22: Amount of FAMEs in various biodiesels and their blends.

Bio

die

sel

sou

rce

% B

iod

iese

l

FAME (mg/mL of oil) C

-6:0

C-8

:0

C-1

0:0

C-1

2:0

C-1

4:0

C-1

4:1

∆ci

s-9

C-1

6:0

C-1

6:1

∆ci

s-9

C-1

8:0

C-1

8:1

∆ci

s-9

C-1

8:1

∆ci

s-11

C-1

8:2

∆ci

s,9,1

2

C-1

8:3

∆ci

s-9,1

2,1

5

C-2

0:0

C-2

0:1

∆ci

s-11

C-2

2:0

C-2

2:1

∆ci

s-13

C-2

4:0

C-2

4:1

∆ci

s-15

Mic

roa

lga

e

B-2 ND ND ND 0.13 0.14 ND 0.31 ND 0.05 ND ND ND 0.04 ND ND ND 0.01 ND ND

B-5 ND ND ND 0.23 0.24 ND 0.55 ND 0.09 0.28 ND 0.25 0.08 ND ND ND 0.03 0.01 ND



Ca

sto

r o

il

B-2 ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND

B-5 ND ND ND ND ND ND 0.02 ND 0.02 ND ND ND ND ND ND ND ND ND ND

B-20 ND ND ND ND ND ND 0.03 ND 0.03 0.20 ND 0.10 ND ND ND ND ND ND ND


Co

con

ut

B-2 0.43 1.53 0.89 6.99 2.95 ND 1.13 ND 0.32 0.81 0.85 0.31 ND ND ND ND ND ND ND




Co

rn

B-2 ND ND ND ND ND ND 0.02 ND ND ND ND ND ND ND ND ND ND ND ND

B-5 ND ND ND ND ND ND 0.03 ND ND 0.09 ND ND ND ND ND ND ND ND ND


B-100 ND ND ND ND ND ND 0.34 ND 0.12 1.69 0.39 1.23 ND ND ND ND ND ND ND


Page | 105

Table 22 (Cont.)

Co

tto

n s

eed

B-2 ND ND ND ND ND ND 0.04 ND ND 0.10 ND ND ND ND ND ND ND ND ND




Lin

seed

B-2 ND ND ND ND 0.02 ND 0.82 ND 0.69 2.62 3.13 1.59 7.57 0.03 ND ND ND ND ND




Nee

m

B-2 ND ND ND ND 0.02 ND 0.02 ND 0.02 0.13 ND ND ND ND ND ND ND ND ND

B-5 ND ND ND ND 0.03 ND 0.04 ND 0.03 0.17 ND ND ND ND ND ND ND ND ND

B-20 ND ND ND ND 0.03 ND 0.15 ND 0.10 0.35 0.28 ND ND ND ND ND ND ND ND

B-100 ND ND ND ND 0.07 ND 0.85 ND 0.60 2.43 1.56 ND ND 0.05 ND ND ND ND ND

Pea

nu

t

B-2 ND ND ND ND ND ND 0.99 ND 0.41 4.52 5.52 3.33 ND 0.18 0.19 0.30 ND 0.16 ND




Ra

pes

eed

B-2 ND ND ND ND ND ND 0.02 ND ND ND ND ND ND ND ND ND ND ND ND

B-5 ND ND ND ND 0.01 ND 0.03 ND ND 0.20 0.10 ND ND ND 0.03 ND ND ND ND

B-20 ND ND ND ND 0.03 ND 0.13 ND 0.04 0.65 0.66 ND 0.06 ND 0.07 ND ND ND ND

B-100 ND ND ND ND 0.10 ND 0.45 ND 0.18 2.84 2.07 0.28 0.27 ND 0.31 ND ND ND ND


Page | 106

Table 22 (Cont.)

So

yb

ean

B-2 ND ND ND ND ND ND 0.56 ND 0.60 1.21 1.35 6.77 0.26 0.04 0.04 0.09 ND ND ND




Su

nfl

ow

er

B-2 ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND 0.03 ND ND

B-5 ND ND ND ND ND ND 0.01 ND ND ND ND ND ND ND 0.02 ND 0.07 ND ND

B-20 ND ND ND ND ND ND 0.02 ND 0.01 0.29 ND ND ND ND 0.08 0.01 0.28 ND ND

B-100 ND ND ND ND ND ND 0.08 ND 0.06 1.46 0.11 ND ND 0.04 0.38 0.05 1.42 ND ND

Wa

ste

coo

kin

g o

il B-2 ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND 0.01 ND ND

B-5 ND ND ND ND ND ND 0.01 ND ND ND ND ND ND ND ND ND 0.03 ND 0.01

B-20 ND ND ND ND ND ND 0.02 ND 0.01 0.19 ND ND ND ND 0.03 ND 0.11 ND 0.06

B-100 ND ND ND ND ND ND 0.10 ND 0.05 0.95 0.21 0.11 ND 0.02 0.15 0.02 0.56 0.01 0.20

Bio

die

sel-

bio

die

sel

ble

nd

BB-2 ND ND ND ND ND ND 0.19 ND 0.13 0.43 0.74 0.42 0.12 0.03 0.02 0.03 0.02 ND ND

BB-5 0.17 0.33 0.23 1.54 0.69 ND 0.73 0.02 0.35 1.68 1.94 2.47 0.38 0.06 0.07 0.07 0.06 ND ND

BB-20 0.23 1.14 0.70 2.38 2.29 0.04 2.45 0.04 1.14 5.08 6.22 8.07 1.31 0.18 0.17 0.30 0.21 ND 0.07

BB-100 1.73 5.10 3.13 25.62 11.24 0.16 12.22 0.20 5.71 26.60 31.77 40.28 6.17 0.87 0.99 1.47 1.12 ND 0.33

*ND=Not Detected


Page | 107

3.7 Conclusion

Biodiesel is gradually replacing diesel fuel and widely using in blended form with diesel.

In this study, biodiesels (B-100) from various sources including corn, sunflower,

soyabean, rapeseed, peanut, coconut, castor seeds, cotton seeds, linseed, neem tree seeds,

waste cooking oil and microalgae were produced by base catalyzed transesterification

reaction. Biodiesel-diesel blends (B-2, B-5 and B-20) and biodiesel-biodiesel blends

(BB-2, BB-5, BB-20 and BB-100) were prepared (v/v). The gas chromatographic tandem

mass spectrometric method (GC-MS/MS) was developed and validated in various

biodiesels, biodiesel-diesel blends and biodiesel-biodiesel blends for the analysis of

FAMEs. Linear relationships between the concentration and the corresponding peak area

were found over the concentration range of 50-500 µg/mL with good correlation

coefficient (r2) (greater than or equal) 0.96 in all cases. The precision never exceeded

5.5% with good assay accuracy.The developed method offers about ten times better

signal–to-noise ratio in all type of biodiesel blends. Selected precursor ion and product

ions were taken into account for the quantification in the developed method therefore

complexity of biodiesel due to change in sources of various biodiesels and their blends

with diesel in different percentages was eliminated from the chromatogram. The

developed method is not only useful for these biodiesels and biodiesel-biodiesel blends

only but can be applied in any biodiesel and its blends.

Page | 108

4 CHAPTER 4

SENSITIVE DETERMINATION OF

GLYCEROL BY POST-DERIVATIZATION

USING HPLC-DAD METHOD IN

BIODIESEL SAMPLES

Chapter 4: Sensitive determination of glycerol in Biodiesel.

Page | 109

4.1 Introduction

Biodiesel is obtained by transesterification of neutral lipids from many biological sources

which gives mixture of fatty acid methyl esters (FAMEs) (biodiesel) and a by-product

glycerol (Stelmachowski, 2011). Although glycerol is removed from biodiesel during

purification step by washing, incomplete washing can leave behind traces of glycerol in

the biodiesel. American Society for Testing and Materials (ASTM), European Union

standards and Brazilian regulatory agency “Agência Nacional do Petróleo” (National

Petroleum Agency, ANP) for biodiesel have established the limit of maximum amount of

free glycerol in biodiesel as 200 mg per kg (0.02%) (Ribeiro and Rocha, 2013).

Therefore, it is necessary to determine the concentration of glycerol after purification

step to check the quality of biodiesel. Presence of glycerol in the finished biodiesel can

cause various severe problems. Some of them are as follow:

Glycerol is polar and hydroscopic in nature and its can cause tank corrosion.

It can cause clogging of fuel filter.

It can damage the combustion system.

It also produces harmful gasses during combustion, such as acrolein.

The standard method for the determination of glycerol in ASTM is D6751-07a whereas

EN 14214 is used by European standards. Although there are other various alternative

methods for the determination of free glycerol in the finished biodiesel. The list of

alternative method for the determination of free glycerol in the biodiesel using various

techniques is summarized in table 23.

Table 23 shows various alternative methods for the determination of free glycerol in

biodiesel using thin layer chromatography (TLC) (Bansal, McCrady, Hansen, and

Bhalerao, 2008), supercritical fluid chromatography (SFC) (Cole, Lefler, and Chen,

2008), high performance liquid chromatography (HPLC) (Blacksmith and Ott, 2013;

Hajek, Skopal, and Machek, 2006; R. Li et al., 2012; R. Li, Wang, Ma, and Chen, 2011),

gas chromatography (GC)(Plank and Lorbeer, 1995), capillary electrophoresis (CE)

(Goncalves Filho and Micke, 2007), spectrophotometry (P. Bondioli and Della Bella,

2005; Ribeiro and Rocha, 2013; S. G. Silva and Rocha, 2010), fluorimetery (Lima et al.,

2012), potential cycling technique (voltammetry) (Lourenco and Stradiotto, 2009) and

amperometry (Maruta and Paixao, 2012) have been reported. Literature survey reveals


Page | 110

that reported HPLC methods quantify glycerol in biodiesel are based on refractive index

(Hajek et al., 2006) and evaporative light scattering detections (Blacksmith and Ott,

2013; R. Li et al., 2012; R. Li et al., 2011) HPLC along with DAD/PDA detector is a

common configuration for most HPLC systems operating in industries and research

laboratories. Glycerol molecule lacks a chromophore to be detected by DAD/PDA

detector and this puts a restriction on its quantification in biodiesel samples using HPLC-

DAD/PDA system. This chapter describes the quantification of glycerol after conversion

into a UV active derivative, glyceryl tribenzoate (GTB) using benzoyl chloride and

copper chloride as a catalyst under mild condition. To the best of our knowledge, this is

the first report describing sensitive analysis of glycerol in various biodiesels through

HPLC- DAD after post-derivatization.

Table 23: Alternative methods for determination of free glycerol in biodiesel.

S.

No. Technique Work Ref.

1. Thin layer

chromatography

(TLC)

1. Thin layer chromatography and

image analysis to detect glycerol in

biodiesel.

(Bansal et al., 2008)

2. Supercritical fluid

chromatography

(SFC)

1. Fast separation of FFA, FAME and

glycerol for biodiesel analysis by

supercritical fluid chromatography.

(Cole et al., 2008)

3. Hyperformance

liquid

chromatography

(HPLC)

1. Comparison of chromatographic

methods for the determination of

bound glycerol in biodiesel (ELSD).

(Foglia, Jones,

Nunez, Phillips, and

Mittelbach, 2004)

2. Determination of free glycerol in

biodiesel (refractive index).

(Hajek et al., 2006)

4. Ion

chromatography

(IC)

1. Glycerol determination in biodiesel

and biodiesel Blends according to

ASTM D 7591.

(J. Gandhi and Wille,

2013)

2.Ion chromatographic determination

of free and total Glycerol in biodiesel

and biodiesel Blends.

(J. Gandhi, Wille,

and Steinbach, 2009)


Page | 111

3. Simple and innovative

methodology for determination of

glycerol in biodiesel and biodiesel

blends (B2-B100) by ion

chromatography

(J. C. Gandhi, Engel,

and Donaldson,

2009)

5. Capillary

electrophoresis

(CE)

1. Development and validation of a

fast method for determination of free

glycerol in biodiesel by capillary

electrophoresis.

(Goncalves Filho and

Micke, 2007)

6. Gas

chromatography

(GC)

1. Simultaneous gas chromatographic

determination of methanol and free

glycerol in biodiesel.

(M. Mittelbach,

Roth, and Bergmann,

1996)

2. Simultaneous gas chromatographic

analysis of total esters, mono-, di-

and triacylglycerides and free and

total glycerol in methyl or ethyl

biodiesel.

(Prados, Rezende,

Batista, Alves, and

Antoniosi, 2012)

7. Spectrophotometry

1. An alternative spectrophotometric

method for the determination of free


(P. Bondioli and

Della Bella, 2005)

2. A single-phase spectrophotometric

procedure for in situ analysis of free


(Ribeiro and Rocha,

2013)

3. Sequential spectrofluorimetric

determination of free and total

glycerol in biodiesel in a

multicommuted flow system.

(Sidnei G. Silva,

Morales-Rubio, de

La Guardia, and

Rocha, 2011a)

4. A flow injection procedure based

on solenoid micro-pumps for

spectrophotometric determination of

free glycerol in biodiesel.

(S. G. Silva and

Rocha, 2010)


biodiesel via solid-phase extraction

and spectrophotometric analysis.

(Mercer and

Halaweish, 2011)

8. Fluorescencespectr

ometery

1. Automatized flow-batch method

for fluorescent determination of free

glycerol in biodiesel samples using

on-line extraction.

(Lima et al., 2012)


Page | 112

9. Potential cycling

technique


biodiesel at a platinum oxide surface

using potential cycling technique.

(Lourenco and

Stradiotto, 2009)

10. Amperometric 2. Flow injection analysis of free

glycerol in biodiesel using a copper

electrode as an amperometric

detector.

(Maruta and Paixao,

2012)

11. Electrochemical

1. Analysis of free and total glycerol

in biodiesel using an electrochemical

assay based on a two enzyme oxygen

electrode system.

(Luetkmeyer et al.,

2010)

2. Analysis of free glycerol in

biodiesel using an electrochemical

assay based on a two-enzyme

platinum microelectrode system.

(Pegas, Amado, de

Castro, and D'Elia,

2010)


Page | 113

4.2 Experimental

4.2.1 Chemicals and Materials

Standard glycerol (assay ≥99.5%), glyceryl tribenzoate (GTB) (95%) and triethyl amine

were purchased from Sigma-Aldrich (USA). Benzoyl chloride (synthesis grade) was

purchased from Scharlau (Spain). HPLC grade methanol, acetonitrile, hexane and ethyl

acetate were purchased from Tedia (USA). Tetrahydrofuran (THF) with sealed septa

dried over molecular sieves (<0.001% H2O) was purchased from ACROS organic

(Belgium). Copper chloride dihydrate, ammonium chloride and magnesium sulfate

anhydrous were purchased from Wako (China). Water was purified using a Millipore®

Milli-Q Plus system (Bedford, USA). Sunflower and peanut oil seeds were purchased

from local market. Dry biomass of mixed microalgal culture was obtained from Pakistan

Council of Scientific and Industrial Research (PCSIR).

4.2.2 Instrumentations

1H NMR:

1H NMR spectra were recorded on a Bruker 300 MHz instrument at 300 MHz

CDCl3 as solvent. Chemical shifts (δ) are expressed in ppm downfield from TMS as

internal standard. The letters s, d, t, q and m are used to indicate singlet, doublet, triplet,

quadruplet and multiplet.

ESI-MS:High resolution mass spectrasing Electrospray ionization (ESI) in positive

mode was performed on Qq-TOF-MS/MS instrument (QSTAR XL mass spectrometer

Applied Biosystem /MDS Sciex, Darmstadt, Germany) at room temperature. High-purity

nitrogen gas was used as the curtain gas and collision gas delivered from Peak Scientific

nitrogen generator. The ESI interface conditions were: Ion spray capillary voltage of

5500 V, curtain gas flow rate 20 L min−1

, nebulizer gas flow rate 30 L min−1

, focusing

potential of 265 V and collision energy was 35 eV. Sample was introduced into the mass

spectrometer using a Harvard syringe pump (Holliston, MA) at a flow rate of 5 µL min−1

.

Microwave Irradiation Experiments: Microwave-assisted synthesis was carried out in

an Initiator 8 single-mode microwave instrument producing controlled irradiation at

2.450 GHz (Biotage AB, Uppsala), including proprietary Workflow Manager Software

(version 2.1). Experiments were carried out in sealed microwave process vials (2 to 5 mL


Page | 114

filling volume) utilizing the standard absorbance level (400 W maximum power).

Reaction times under microwave conditions refer to hold times at the temperatures

indicated, not to total irradiation times. The temperature was measured with an IR sensor

on the outside of the reaction vessel.

Spectrophotometeric Analysis: Synthesized substance was dissolved in acetonitrile

with the concentration ranging from 1.0–0.9 mM. The absorbance maxima was recorded

by the UV/visible scanning in the region of 200-800 nm against the reagent blank on a

UV/Visible double beam spectrophotometer (Thermo scientific evolution 300, U.K).

Preparative HPLC: The synthesized compound was purified via preparative recycling

HPLC system (LC-908, Japan Analytical Industry) on a column Sil-D-60-10 (250 x 20

mm).

HPLC Analysis: HPLC analysis were performed with Agilent 1200 series Rapid

Resolution LC (RRLC) system comprising Agilent binary pump SL with degasser, high

performance autosampler SL with thermostat, thermostatted column compartment

(TCC), diode-array detector SL (DAD SL) and Light evaporative scattering detector

(ELSD). Data acquisition and integration was controlled by Agilent Technologies

ChemStation software. An Agilent Poroshell 120 E-C18 column (50×3 mm I.D., 2.7 μm)

and Zorbax XDB-C8 column (50×4.6 mm I.D., 1.8 μm) was used. The mobile phase was

a binary gradient system prepared from water (eluent A) and acetonitrile (eluent B),

properly filtered and degassed for 15 minutes in ultra sonic bath before use.

4.2.3 Esterification of Glycerol into Glycerol Tribenzoate (GBT)

4.2.3.1 Standard Protocol

A dry 5.0 mL microwave vial equipped with a magnetic stirring bar containing 1.0 mmol

(92 mg) of glycerol was sealed and flushed with nitrogen. After addition of anhydrous

THF (3.0 mL) and Et3N (1.5 mL, 11 mmol) through vial septum, BzCl (1.4 mL, 12

mmol) was added drop-wise with a flow rate of 15 drops/minute to the reaction mixture

at room temperature with constant stirring. After total 1 h (stirring), the resulting mixture

was quenched by a solution of saturated NH4Cl (5 mL). The resulting mixture was

extracted twice with EtOAc (10 mL X 2). The EtOAc extracts were combined and dried


Page | 115

over MgSO4. Then evaporate on under reduced pressure. This protocol is used for the

entry 1-9 in the table 24.

4.2.3.2 Microwave Procedure

Microwave-assisted synthesis was carried out in an Initiator 8 single-mode microwave

instrument producing controlled irradiation at 2.450 GHz (Biotage AB, Uppsala),

including proprietary Workflow Manager software (version 2.1). Experiments were

carried out in sealed microwave process vials (2 to 5 mL filling volume) utilizing the

standard absorbance level (400 W maximum power). Reaction times under microwave

conditions refer to hold times at the temperatures indicated, not to total irradiation times.

The temperature was measured with an IR sensor on the outside of the reaction vessel.

This protocol is used for the entry 10 in the table 24.

4.2.4 Purification of GTB

The solvent was evaporated and the residue was purified by preparative recycling HPLC

system (LC-908, Japan Analytical Industry) on a preparative column Sil-D-60-10 (250 x

20 mm x 5µm) using Hexane/EtOAc (1:4) to provide the desired product GTB as a white

solid. The compound was characterized by recording their mass and1H NMR spectra.

4.2.5 Preparation of Stock and Calibration Standard Solutions

Stock solutions of glycerol and GTB were prepared by accurately weighing 5.0 mg of

each into a 5 mL volumetric flask separately and making up the volume with water and

acetonitrile, respectively. Calibration standard solutions ranging from 3-100 µg/mL of

glycerol and GTB were prepared by dilution of stock solution with water and

acetonitrile, respectively.

4.2.6 Preparation of Biodiesel Samples

Biomass (1kg) of plants seeds including sunflower, peanut and microlagae were

separately soaked for three days in petroleum ether (2L each). Extracts were filtered and

the solvent was removed with rotatory evaporator to obtain oil. The biodiesel was

produced by basic methanolysis of all oils using a methanol/oil molar ratio of 12:1, with

1% potassium hydroxide by weight as the catalyst. The reaction temperature and time

were 60 °C and 1 h, respectively (Keera, El Sabagh, and Taman, 2011). After


Page | 116

completion, the reaction mixture was transferred into a separating funnel, washed

thoroughly with water and biodiesel was extracted with hexane (3x IL) and dried over

anhydrous magnesium sulfate. Hexane was evaporated from the biodiesel by using rotary

evaporator (N-1000, Eyela, Japan).

4.2.7 Derivatization of Biodiesel

1.0 g of each biodiesel was weighed and transferred to a dry 10.0 mL microwave vial

equipped with a magnetic stirring bar. The vial was sealed and flushed with nitrogen.

The sample was derivitized according to method described in section 4.2.3.1. The

resulting mixture was quenched with a solution of saturated NH4Cl (5 mL) and extracted

with EtOAc (10 mL). The EtOAc extracts were combined and dried over MgSO4. The

solvent was evaporated and the residue was dissolved in 5 mL acetonitrile. The solution

was filtered with 0.45 µm PTFE fluoropore syringe driven filter unit (Millipore, Bedford,

USA). Samples were preserved at 4 ºC prior to LC analysis.


Method was validated by using various parameters including accuracy, precision,

sensitivity, robustness, recovery study and selectivity.

4.2.8.1 The Limit of Detection (LOD) and Limit of Quantification (LOQ)

The limit of detection (LOD) and limit of quantification (LOQ) were estimated by using

calibration curve. The LOD and LOQ were calculated according to the following

equation: LOD = 3.3δ/S and LOQ = 10δ/S, where δ = the residual standard deviation of a

regression line or the standard deviation of Y-intercepts of regression line and S = the

slope of the calibration curve.

4.2.8.2 Accuracy and Precision

Accuracy and precision were determined at three different standard concentration levels

including 80, 50 and 20 µg/mL. Method repeatability was evaluated in terms of

coefficient of variance (CV) by repeating the analysis on the same day for intra-day

precision. Intermediate precision was assessed by the analysis of same standard on

different day (inter-day precision).


Page | 117

4.2.8.3 Robustness

Robustness of the method was determined by varying in the mobile phase composition,

wavelength, flow rate, and temperature of column. All these parameters were varied

within a range of ±5%. Mobile phase composition and flow rate were varied at initial and

final state of gradient system. The effect of these variations on the result was examined

as robustness of the method.

4.2.8.4 Recovery Studies

For recovery studies, pre-analyzed samples were again prepared and spiked with known

amount of the standard glycerol and the mixtures were analyzed by the developed

method. Microlagal biodiesel sample were selected for conducting recovery studies.

Recovery was calculated by the following equation: Recovery (%) = (sample contents

after adding − original contents)/contents of standard solution for adding x 100.

4.2.8.5 Selectivity

The selectivity of the method was ascertained by overlaying the chromatograms of

standard, and various biodiesel samples. The peak for GTB in samples was confirmed by

comparing the retention time and UV spectra of the peak with that of standard.


Page | 118


4.3.1 Reaction Optimization for Esterification

For reaction optimization, a well-planned examination by varying reactants amount at

different temperatures was performed. Reaction was started at 1 mmol scale with pure

glycerol sample while using 3.5 equiv of esterification reagent (benzoyl chloride), 12

mol% CuCl2. 2H2O as a catalyst and 1.5 equiv Et3N as a base. Initial experiments

showed that three possible esterified products of glycerol can be observed as glycerol

tribenzoate (3), dibenzoate (4) and monobenzoate (5). However, presence of a single UV

active esterification product for quantification of glycerol in biodiesel samples was

necessary. Experiments showed that stoichiometric amount of benzoyl chloride (BzCl)

and Et3N were required for complete conversion of glycerol into glyceryl tribenzoate (3)

as shown in Table 24 (entry 9). The reaction time was monitored for glyceryl tribenzoate

(3) at room temperature and completion was found in 1 hour. With considerable

experiments, it was quickly realized that CuCl2. 2H2O catalyst was required 17 mol% in

optimum. Stoichiometric amount of BzCl (12 equiv) was needed for promoting complete

conversion to glyceryl tribenzoate (3). Reducing the amount of BzCl, resulted into

predominant formation of mono- and di-tribenzoate by-products (Table-24, entry 2-3).

To our gratification, we explored that the amount of Et3N was effectively controlling the

reaction completion and selectivity of product and by-products. After substantial efforts,

it was identified that 11 equiv of Et3N provided complete conversion (100%) into a

single tribenzoate (3) product. Noticeably, HPLC conversion reflected the comparable

isolated yield of compound (3) (Table 24). For example in entry 9, 90% yield of glycerol

tribenzoate (3) was obtained after preparative HPLC. HPLC-ELSD chromatogram also

confirmed the complete conversion of glycerol into GTB and showed peak for standard

glycerol at RT 0.59934 ± 0.032 min whereas this peak disappeared from reaction mixture

chromatogram of after derivitization (Figure 34). GTB was identified by correlating

NMR spectra and mass spectrometric data of the product with the standard compound.

The use of higher reaction temperatures using microwave irradiation did not improve the

efficiency of this transformation and led to a diminished amount of product due to the

formation of undesired by-products (Table 24, entry 10 vs 5-6).


Page | 119

Table 24: Esterification of glycerol into glyceryl tribenzoate .

Entry CuCl2.

2H2O

(mol%)

BzCl

(equiv)

Et3N

(equiv)

Product 3b

(%)

By-products

4+5b

(%)

1 12 3.5 1.5 <10 24

2 15 3.5 1.5 <10 28

3 15 6.0 3.0 15 35

4 15 12.0 3.0 19 45

5 30 12.0 3.0 20 48

6 17 12.0 5.5 35 50

7 17 12.0 7.0 56 28

8 17 12.0 10.0 95 traces

9 17 12.0 11.0 100c

_

10 30 12.0 5.0 <10d

_

aReaction conditions: 1 mmol of glycerol 1, 3.0 mL THF, stirring at room temperature

for 1 h. bProduct distribution refers to relative peak area (%) ratios of crude HPLC-UV

(239 nm) traces. c

Product isolation by preparative HPLC provided an 90% yield of

glyceryl tribenzoate 3. d

Reaction conditions: sealed-vessel, single-mode, microwave

irradiation at 120 °C for 15 min.

HC

H2C

H2C

OH

OH

OH

CuCl2 .2H2Ort, 1h

+ Cl

O

HC O

H2C O

H2C O

O

O

O

1 2 3

HC O

H2C OH

H2C O

O

O

4

HC OH

H2C OH

H2C O

O

5

+ +


Page | 120

Figure 34: HPLC-ELSD chromatrgmn of (A) standard glycerol (B) reaction mixture.

4.3.2 Isolation and Characterization of Glyceryltribenzoate (GTB)

The crude product was purified by preparative recycling HPLC system on a preparative

column Sil-D-60-10 (250 x 20 mm x 5µm) using Hexane/EtOAc (1:4) to provide the

desired product GTB as a white solid. 1H NMR (300 MHz, CDCl3): δ 8.05- 7.99 (m, t, J

= 7.2 Hz, 6H), 7.57-7.52 (m, 3H), 7.44-7.38 (m,6H), 5.81 (m, 1H), 4.69 (dd, J = 12.0, 4.5

Hz, 4H). HR-ESI-MS (m/z): 405.1321 (405.1338 calcd. for C24H21O6).

4.3.3 HPLC Method Optimization.

Determination of λ max for GTB: For λ max determination of GTB, the wavelength

range from 190 to 800 nm was scan with UV spectrophotometer. The wavelength 238

nm was found to be λ max for GTB. The UV scan spectrum is shown in Figure 35.


Page | 121

Figure 35: UV scan for GTB.

Development of the Optimum Mobile Phase: The HPLC procedure was optimized to

develop an accurate, reliable and rapid method for the analysis GTB (derivitized

glycerol) in the biodiesel samples. For this purpose, several chromatographic conditions

were checked. The standard GTB and biodiesel samples were run in different solvent

systems including water, methanol and acetonitrile. Water-acetonitrile with different

ratios were tried in order to improve resolution and minimization of the analysis time,

about 30 different gradient systems were tested. Methanol-water system was also used

but due to high pressure and poor separation this system was rejected. Different reverse

phase columns were used with varying the temperature of the column compartments as

well as the different gradient systems were used but the best results were obtained by

using the stationary as Agilent Poroshell 120 E-C18 column (50×3 mm I.D., 2.7 μm).

The optimized gradient mobile phase is given in Table 25.


Page | 122

Table 25: Optimized gradient mobile phase.

Time

(min)

A

(H2O)

B

(HCN)

Flow rate

(mL/min)

0 90 10 0.7

6 0 100 0.7

6.5 90 10 0.7

8 90 10 0.7

This system showed sharp and symmetrical peak for GTB at 5.9534 ± 0.022 (Figure 36).

HPLC chromatograms ofstandard, blank reaction mixture derivatized standard glycerol

and derivitized sunflower biodiesel can be visualized in Figure 36. Biodiesels samples

becomes a complex mixtures after derivitization reaction but the developed optimized

HPLC conditions provide well separated peak of GTB other constituents present in the

sample for such a complex mixtures.

Figure 36: Chromatogram of (A) standard (B) blank (C) derivatized standard glycerol

and (D) derivitized sunflower biodiesel, Peak 1 GTB (Rt: 5.9534 ± 0.022).


Page | 123

4.3.4 Calibration Curves of Standard GTB

Linearity of an analytical method is its capacity to attain test responses that are directly

proportional to the amount of analyte present in the sample. In order to demonstrate this,

standard solution of the test substances are diluted or separately weighed. Linearity is

determined by 5-6 replicates injections of multiple standards (usually 5 or more) with the

concentrations ranging between 80–120 percent of the anticipated range of

concentration. The amount of the standards must be directly proportional to the detector

response or proportional via well-defined mathematical model. Linear regression

equations when applied to the obtained responses should not have an intercept that

deviate significantly from zero.

Table 26: Calibration data of GTB

Calibration Levels

(µg/mL) Area Mean SD CV

100

478.91

478.53 0.34 0.07 478.42

478.25

80

386.24

384.77 1.65 0.43 382.99

385.08

50

238.53

238.43 0.14 0.06 238.49

238.27

20

95.74

95.71 0.02 0.02 95.71

95.69

5

24.86

24.6 0.49 2.01 24.92

24.04

3

16.48

16.57 0.10 0.60 16.56

16.68


Page | 124

The linear concentration range for GTB was from 3-100 µg/mL with correlation

coefficient of 0.9999. This linearity was evaluated by six standard working solutions.

The calibration graph with concentration on x-axis and peak area on y-axis is shown in

Figure 37. All 6 concentration levels and amount injected for GTB are given in Table 26.

Peak area and concentration was subjected to the least square linear regression analysis

and the results are shown in Table 27. The mean value of slope and intercept forGTB

were found to be 4.787 and 0.912, respectively.

Figure 37: Calibration curve f GTB

Table 27: Linear regression data for the calibration curves.

Retention time

(min)

Linear range

(µg/mL)

Regression equation r2

5.9934 ± 0.022 3-100 y = 4.787x + 0.912 0.999

y = 4.787x + 0.912R² = 0.999

0

100

200

300

400

500

600

0 20 40 60 80 100 120

Are

a

Concentration (µg/mL)


Page | 125


The process which is used for the confirmation of method‟s suitability for a specific is

called method validation. Method validation showed the quality, reliability and

consistency of analytical results. It is an integral part of any good analytical practice.

There is need of method validation when method is developed or revalidation prior to

their routine utilization whenever the conditions change for which the method has been

validated.The developed method was validated against following parameters including

accuracy, precision, limit of detection, limit of quantification, linearity, reproducibility

and robustness.

4.3.5.1 Limit of Detection (LOD) and Limit of Quantification (LOQ)

The Limit of detection (LOD) of an analytical method is defined as the minimum

quantity of an analyte in a given sample which can be detected but not certainly

quantified with a precise value.In the developed method, LODs was found to be 3.45

µg/mL.

The limit of quantitation (LOQ) of a specific analytical method is defined as the

minimum quantity of an analyte in a given sample which can be determined

quantitatively with acceptable accuracy and precision.In the developed method,LOQs

was found to be 10.46 µg/mL for GTB.

4.3.5.2 Precision and Accuracy

Precision of an analytical method means that how much close agreement is present in

measurements acquired from multiple analyses of sample under given conditions.

Precision is measured under three ranks that are repeatability, intermediate precision and

reproducibility. Accuracy is the agreement between the found value and true value.

Accuracy is usually calculated by comparison of the results obtained with results of a

method already established. For the developed method, the precision was expressed in

terms of percentage relative standard deviation (% R.S.D.). Precision was determined

with two different analysts while reproducibility was determined by intra-day and inter-

day analysis of GTB.

For the intra-day studies, three different levels of standard concentrations 80, 50 and 20

µg/mL were selected and three runs for each standard were recorded. First run was


Page | 126

carried out in the early day time and the second run was carried out in the mid-day and

the third run was carried out in late day time. The precision was expressed in terms of

percent relative standard deviation (% R.S.D.). The accuracy was also determined and

expressed in %. The day 1 results are summarized in table 28 for intraday analysis. These

results showed no significant variation for three different standard concentration levels

for intraday day analysis. The Intra-day analysis data for is given in Table 28. The %

R.S.D. for intra-day analysis for was found to be 0.39 % in all cases. The accuracy of the

method was in between 99.01-100.04%for intra-day analysis.

The inter-day analysis was also performed with the same concentration levels of standard

as they were used in intra-day i.e. 80, 50 and 20 µg/mL. The mentioned three

concentrations were run in a single day and its results were noted and these three

concentrations were rerun the other day and precision was expressed in terms of percent

relative standard deviation (% R.S.D.). The accuracy was also determined and expressed

in %. The results of Inter-day analysis are summarized in Table 28. The method showed

no significant variation for GTB for three different standard concentration levels for

inter-day analysis. The % R.S.D. for intra-day analysis for GTB was found to be <

0.43% in all cases. The accuracy of the method was in between 99.04-100.62% for inter-

day analysis.

Table 28: Intra-day and Inter-day analysis of GTB.

Conc.

(µg/mL)

DAY 1 DAY 2

Found

(µg/mL)

R.S.D.

(%)

Accuracy

(%)

Found

(µg/mL)

R.S.D.

(%)

Accuracy

(%)

80.00 80.03 ± 0.31 0.39 100.04 80.19 ± 0.34 0.43 100.62

50.00 49.61 ± 0.03 0.06 99.22 49.62 ± 0.03 0.06 99.28

20.00 19.80 ± 0.00 0.01 99.01 19.80 ± 0.00 0.02 99.04


Page | 127

4.3.5.3 Robustness

The capacity to tolerate small intentionally induced change in a method of an analytical

procedure is called robustness. It shows that how much a method is reliable. Robustness

actually is an assessment of an analytical procedure to detect small variations in

operational parameters. Robustness for HPLC method is determined by varying the

parameters like injection volume, column temperature, composition of the mobile phase

and detection wavelength. If the effect produced by these variations lies in tolerance

range of the method then the method is considered validated for robustness test. If these

values do not lie in the tolerance range then the method needs to be revalidated.

In our method, robustness was evaluated by the tuning of various parameters such as

mobile phase composition, flow rate and temperature within ±5% variation from the

proposed method. The standard deviation of peak area was calculated for each parameter

and % R.S.D. was found to be less than 2.4% which indicated robustness of the method.

The data of the all parameters for robustness are summarized in Table 29.

Table 29: Summary of the robustness parameters of developed method.

Parameter Modification Standard

deviation

CV

(%)

Mobile phase composition

10.5:89.5

0.49 2.01 10:90

9.5:90.5

Temperature

31.5

0.04 0.15 30

28.5

Wavelength

238

0.62 2.53 239

240

Flow rate

0.74

0.58 2.39 0.70

0.67


Page | 128

4.3.5.4 Recovery Studies

Recovery studies involve the addition of a known amount of analyte to a sample and then

determining what percent of the amount added is detected. Recovery studies showed

matrix effect. It was checked by the estimation of GTB from microalgal biodiesel

samples after spiked with known amount of additional standard. Recovery percent was

found to be 96.66-111.44 % with percent relative standard deviation 0.05-3.16

respectively, as listed in Table 30.

Table 30: Recovery studies of GTB (n=3).

Theoretical content

(μg/mL)

Recovery (%)

Mean Recovery (%) R.S.D. (%)

1 2 3

0.500 97.20 96.66 96.13 96.66 ± 0.53 0.55

0.100 105.98 101.16 107.11 104.75 ± 3.16 3.02

0.050 98.00 99.64 97.62 98.42 ± 1.07 1.09

0.020 106.50 105.00 110.00 107.17 ± 2.57 2.39

0.010 110.00 110.00 114.33 111.44 ± 2.50 2.24

4.3.5.5 Selectivity

This is the ability to unequivocally assess an analyte in the presence of components

which may be expected to be present. Typically this might include impurities,

degradants, matrix, etc. Selectivity of GTB was assessed by overlaying the spectra of

standard and biodiesel of various sources as shown in Figure 38. The overlaid spectra

showed that there is no other peak at the retention time of GTB.


Page | 129

Figure 38: Overlaid chromatograms of standard (blue), microagal biodiesel (green), peanut biodiesels (red) and sunflower biodiesel (pink)


Page | 130

4.3.6 Analysis of Glycerol in Biodiesel Samples

The developed method was applied for quantification of glycerol in various biodiesel

samples. These biodiesel samples include microagal biodiesel, peanut biodiesel and

sunflower biodiesel. All biodiesel were prepared in the laboratory by transestrification

reaction. In all six biodiesel samples, peak at Rt5.969 ± 0.003 min were observed in the

chromatogram for GTB along with other components. GTB appears in the chromatogram

at significantly different Rt values as shown in figure 39. The free glycerol content in the

biodiesel samples was found to be 0.004-0.359% (%w/w) of biodiesel (Table 31).

According to the United states and European Union standard for biodiesel only MB-1

and SB-1 found within the limit as they have <0.02% free glycerol whereas other

biodiesels have much higher concentration of free glycerol, which may be due to the

improper washing of the biodiesel during purification step.

Table 31: Analysis of biodiesel samples.

Biodiesel samples (Code) Glycerol (%w/w)

Microalgal biodiesel 1 (MB-1) 0.004 ± 0.001

Microalgal biodiesel 2 (MB-2) 0.327± 0.008

Sunflower biodiesel 1(SB-1) 0.005 ± 0.001

Sunflower biodiesel 2 (SB-2) 0.359± 0.010

Peanut biodiesel 1(PB-1) 0.086± 0.004

Peanut biodiesel 2 (PB-2) 0.229± 0.001


Page | 131

Figure 39:Chromatogram of microalgal (A), sunflower (B) and peanut (C) biodiesel samples,

peak 1 is of GTB (derivitized glycerol) (Rt: 5.969 ± 0.003).


Page | 132

4.3.7 Comparison of Developed HPLC Method with Other Reported Techniques.

Comparative analysis of glycerol using HPLC-DAD after post derivatization with other

reported techniques is summarized in Table 32. The comparison shows that in our

HPLC-DAD method, limit of detection (LOD) and coefficient of variance (CV) is better

than other reported techniques except automatized flow-batch method for fluorescent

determination of free glycerol (Lima et al., 2012). Gratifyingly, the developed method is

ten times more sensitive than other HPLC methods employing evaporative light

scattering detector and refractive index detector.

Table 32: Comparison of glycerol analysis using HPLC-DAD after post derivatization

with other techniques.

Technique Linear range

(ppm)

LOD

(ppm)

CV

(%)

References

HPLC-DAD 0.6-22.7 0.2 0.4 Present work

HPLC-ELSD 7.1-307.3 2.5 2.0 (R. Li et al., 2012)

HPLC-RI - 2 1.0 (Hajek et al., 2006)

GC 1.0-6.0 - 1.3 (Plank and Lorbeer, 1995)

CE 12-82 4.3 1.1 (Goncalves Filho and Micke,

2007)

Spectrophotometry

4-80 0.4 2.1 (Ribeiro and Rocha, 2013)

25-150 - - (P. Bondioli and Della Bella,

2005)

5-50 1.0 1.5 (S. G. Silva and Rocha, 2010)

TLC - 2000 - (Bansal et al., 2008)

Fluorimetry 5-75 0.5 1.0 (Sidnei G. Silva, Morales-

Rubio, de La Guardia, and

Rocha, 2011b)

0.1-5.0 0.04 1.5 (Lima et al., 2012)

Voltammetry 15-150 2.3 0.7 (Lourenco and Stradiotto,

2009)

Amperometry 3-160 0.25 5.0 (Maruta and Paixao, 2012)


Page | 133

4.4 Conclusion

Biodiesel is obtained by transesterification of neutral lipids from many biological sources

which gives biodiesel and a by-product glycerol. International biodiesel standard

agencies have established the limit of maximum amount of free glycerol in biodiesel.

Therefore, it is necessary to determine the concentration of glycerol to check the quality

of biodiesel. HPLC along with DAD/PDA is a common configuration for most HPLC

systems operating in industries and research laboratories. Glycerol molecule lacks a

chromophore to be detected by DAD/PDA detectors. The major impact of developed

method for quantification of glycerol in various biodiesel after conversion into a UV

active derivative under mild condition and utilized most common HPLC configuration

i.e. HPLC-DAD/PDA. Moreover, this method can be used as an alternative method for

HPLC-RI and HPLC-ELSD with ten times enhanced sensitivity. A broard range of

biodiesel samples can be analyzed within 8 minutes run by using a single HPLC method

and followed by simple and low cost derivatization procedure for sample

preparation.The limit of detection and limit of quantification were found to be 0.23

µg/mL and 0.76 µg/mL, respectively. The developed HPLC-DAD method is accurate,

precise and robust for the determination of glycerol in the biodiesels. Statistical data

showed that the method is reproducible and selective for the quantification of target

analytes. The developed method can be used by the oil and petroleum industries or the

national authority dealing with biodiesel analysis

.

Page | 134

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Page | 149

GLOSSARY

Glossary

Page | 150

Acid value:The milligrams of potassium hydroxide that is required to neutralize one

gram of chemical substance. It is a measure the amount of carboxylic acid groups in a

chemical compound or in a mixture of compounds.

Analyzer: The analyzer is the section of the mass spectrometer in which ions (formed in

the ion source) are separated on the basis of their mass-to-charge ratios.

Base peak: In the mass spectrum, the most intense peak is referred to as the base peak

and is used as standard to measure the intensities of other ion-peaks.

Biodiesel: Vegetable oil or animal fat based diesel fuel consisting of long chain alkyl

(methyl, ethyl, or propyl) esters.

Biodiesel blends: The mixture of mineral diesel or other biodiesel is called biodiesel

blends.

Cetane number: It is an indicator of the combustion rate of diesel fuel. It determines the

quality of diesel fuel.

Collision cell: A region of relatively higher pressure placed after the first analyzer in an

MS/MS instrument for the collision induced dissociation of precursor ions. It is usually a

multiple rod transmission device to which appropriate radio frequency voltages are

applied to assist in focusing the ions.

Collision gas: An inert gas introduced at relatively high pressure into a collision cell to

promote decomposition of the precursor ions.

Collision induced dissociation (CID): In a collision between an ion and a neutral

species, a portion of the ion translational energy is converted to internal energy. This

internal energy causes dissociation of the ion into smaller fragment ions and can also

cause changes in the ion charge. Collision-induced dissociation is also known as

collisionally activated dissociation.

Glossary

Page | 151

Data acquisition: The process of converting the MS signals into a form that can be used.

The signals can be acquired in one of several modes, including full scanning, selected

ion monitoring and selected reaction monitoring.

Daughter ion: A secondary ion produced in an MS/MS mode by collision induced

dissociation fragmentation of the precursor ion (parent ion).

Detector: A device that detects the ions produced in the mass spectrometer and produces

a measurable signal generally an electronic signal. Common types include the Faraday

cup, the electron multiplier, the micro channel plate detector and the photomultiplier

detector.

Derivatization: Derivatization is the process of chemically modifying a compound to

produce a new compound which has properties that are suitable for analysis using a GC

or HPLC.

Fragmentation: Fragmentation is a process that occurs when enough energy is

concentrated in a bond. In general, fragment resulting from weak bonds are prominent in

the mass spectra. Fragmentation usually occurs in the ionization source.

Gas chromatography: A gas chromatograph (GC) is an analytical instrument that

measures the content of various components in a sample. It is a chromatographic

technique that can be used to separate organic compounds that are volatile.

GC-MS/MS: When a second phase of mass fragmentation is added, for example using a

second quadrupole in a quadrupole instrument, it is called tandem MS (MS/MS).

Ion source: The part of the mass spectrometer used for sample ionization, since only

particles which carry an electric charge can be analyzed in a mass spectrometer. A

variety of ion sources are in common use and each is designed to ionize a specific class

of atom or molecules.

Kinematic viscosity: The ratio of the dynamic viscosity to the density of the fluidis the

kinematic viscosity.

http://www.files.chem.vt.edu/chem-ed/sep/chromato.html

http://www.files.chem.vt.edu/chem-ed/sep/sepintro.html

Glossary

Page | 152

Magnetic sector analyzer: A direction focusing device that produces a magnetic field

perpendicular to the direction of movement of ion. It works to bring all ions of a given

mass-to-charge ratio to a common point.

Mass spectrometry: Mass spectrometry (MS) is an analytical technique that measures

the mass-to-charge ratio of charged particles.

Matrix-assisted laser desorption ionization (MALDI): MALDI is the formation of

gas-phase ions from molecules in a solid or liquid matrix that is irradiated with a laser.

The matrix is a material that absorbs the laser energy and aids in the formation of free

ions.

Microalgae: Microalgae or Microphytes are microscopic algae, found in freshwater and

marine systems. They are unicellular species, exist individually, or in chains or groups.

Molecular ion (M+): After the electron bombardment on an organic molecule, it readily

loses one electron to produce a molecular ion, which represents its molecular mass.

Precursor ion:Ion that reacts to form particular product ions or undergoes specified

neutral losses.

Quadrupole mass analyzer: A mass filter that creates a quadrupole field with a

direction current (DC) component and a radio frequency component in such a manner as

to allow scanning over a selected mass-to-charge range.

Signal-to-noise ratio (SNR):It is a measure of signal strength relative to background

noise. The ratio is usually measured in decibels (dB).

Saponification value: The number of milligrams of potassium hydroxide required to

saponify 1g of fat under thespecified conditions is known as saponification value. It is a

measure of the average molecular weight (or chain length) of all the fatty acids present.

Tandem mass spectrometry: The use of coupling two or more mass analyzers

separated by a region in which ions can be induced to fragmentation by transfer of

energy

http://en.wikipedia.org/wiki/Mass-to-charge_ratio

http://mass-spec.lsu.edu/msterms/index.php/Ion

http://mass-spec.lsu.edu/msterms/index.php/Product_ion

Page | 153

Personal Introduction

I was born in Karachi on 21st September in 1984in a warm hearted and

supporting family, and a city which provided me, a sound education and

opportunities for personal growth. I got my Secondary School Certificate

(S.S.C.) in 2000 from National Public School, Malir Karachi with „A‟

grade and Higher School Certificate (HSC) in 2002, with 1st division from

Govt. Degree Science College, Malir Cantt, Karachi.I felt my interest in

chemistry because of my college chemistry teachers therefore I chose this

subject for specialization. I passed my B. Sc. (Honors) in 2005 and M. Sc. (Organic Chemistry) in the

year 2006 from the Chemistry Department, University of Karachi with 1st division. For the

completion of my M. Sc. Thesis, I got oppurtunity to work in H.E.J. Research Institute of Chemistry

under the supervision of Prof. Dr. Iqbal Choudhary as a collaborative student. As I had already a

plan for higher studies but this experience inspired and introduced me from H.E.J. Research Institute

of Chemistry therefore for advanced study, I took admission in H.E.J. Research Institute of

Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, as an

M. Phil./Ph. D. research student in April, 2007. I was fortunate to carry out my research under the

kind supervision of Dr. Syed Ghulam Musharraf. During this period, I have attended many

conferences and presented many research findings as posters. Moreover, I have got experience to

handle GC-QQQ, HPLC-DAD-ELSD and other various instruments and I am confident in operating

these sophisticated instruments individually. It is indeed a great fortune that Almighty Allah gave me

an opportunity and strength with steadiness to fulfil requirements of Ph. D. and I always feel

confident on my abilities and qualities, blessed by Almighty Allah, embrace from my parents and later

enhanced by my teachers.

During my research experiences, I have come to understand the importance of respecting others. I will

always remember my stay at this amazing institute, and my supervisor for his kind support at every

step. In future, I intend to play my role in national development as a researcher and teacher.

Muhammad Arif Ahmed

Karachi, 2015

prr.hec.gov.pkprr.hec.gov.pk/.../1/muhammad_arif_ahmed_chemistry... · thesis certificate this is...

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